Oracle University Podcast
Oracle University Podcast delivers convenient, foundational training on popular Oracle technologies such as Oracle Cloud Infrastructure, Java, Autonomous Database, and more to help you jump-start or advance your career in the cloud.
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Understanding Security Risks and Threats in the Cloud - Part 1
11/18/2025
Understanding Security Risks and Threats in the Cloud - Part 1
This week, Lois Houston and Nikita Abraham are joined by Principal OCI Instructor Orlando Gentil to explore what truly keeps data safe, and what puts it at risk. They discuss the CIA triad, dive into hashing and encryption, and shed light on how cyber threats like malware, phishing, and ransomware try to sneak past defenses. Cloud Tech Jumpstart: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------ Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hey everyone! Last week, we discussed how you can keep your data safe with authentication and authorization. Today, we’ll talk about various security risks that could threaten your systems. 00:48 Lois: And to help us understand this better, we have Orlando Gentil, Principal OCI Instructor, back with us. Orlando, welcome back! Let’s start with the big picture—why is security such a crucial part of our digital world today? Orlando: Whether you are dealing with files stored on a server or data flying across the internet, one thing is always true—security matters. In today's digital world, it's critical to ensure that data stays private, accurate, and accessible only to the right people. 01:20 Nikita: And how do we keep data private, secure, and unaltered? Is there a security framework that we can use to make sense of different security practices? Orlando: The CIA triad defines three core goals of information security. CIA stands for confidentiality. It's about keeping data private. Only authorized users should be able to access sensitive information. This is where encryption plays a huge role. Integrity means ensuring that the data hasn't been altered, whether accidentally or maliciously. That's where hashing helps. You can compare a stored hash of data to a new hash to make sure nothing's changed. Availability ensures that data is accessible when it's needed. This includes protections like system redundancy, backups, and anti-DDoS mechanisms. Encryption and hashing directly support confidentiality and integrity. And they indirectly support availability by helping keep systems secure and resilient. 02:31 Lois: Let’s rewind a bit. You spoke about something called hashing. What does that mean? Orlando: Hashing is a one-way transformation. You feed in data and it produces a unique fixed length string called a hash. The important part is the same input always gives the same output, but you cannot go backward and recover the original data from the hash. It's commonly used for verifying integrity. For example, to check if a file has changed or a message was altered in transit. Hashing is also used in password storage. Systems don't store actual passwords, just their hashes. When you log in, the system hashes what you type it and compare the stored hash. If they match, you're in. But your actual password was never stored or revealed. So hashing isn't about hiding data, it's about providing it hasn't changed. So, while hashing is all about protecting integrity, encryption is the tool we use to ensure confidentiality. 03:42 Nikita: Right, the C in CIA. And how does it do that? Orlando: Encryption takes readable data, also known as plaintext, and turns it into something unreadable called ciphertext using a key. To get the original data back, you need to decrypt it using the right key. This is especially useful when you are storing sensitive files or sending data across networks. If someone intercepts the data, all they will see is gibberish, unless they have the correct key to decrypt it. Unlike hashing, encryption is reversible as long as you have the right key. 04:23 Lois: And are there different types of encryption that serve different purposes? Orlando: Symmetric and asymmetric encryption. With symmetric encryption, the same key is used to both encrypt and decrypt the data. It's fast and great for securing large volumes of data, but the challenge lies in safely sharing the key. Asymmetric encryption solves that problem. It uses a pair of keys: public key that anyone can use to encrypt data, and a private key that only the recipient holds to decrypt it. This method is more secure for communications, but also slower and more resource-intensive. In practice, systems often use both asymmetric encryption to exchange a secure symmetric key and then symmetric encryption for the actual data transfer. 05:21 Nikita: Orlando, where is encryption typically used in day-to-day activities? Orlando: Data can exist in two primary states: at rest and in transit. Data at rest refers to data stored on disk, in databases, backups, or object storage. It needs protection from unauthorized access, especially if a device is stolen or compromised. This is where things like full disk encryption or encrypted storage volumes come in. Data in transit is data being sent from one place to another, like a user logging into a website or an API sending information between services. To protect it from interception, we use protocols like TLS, SSL, VPNs, and encrypted communication channels. Both forms data need encryption, but the strategies and threats can differ. 06:19 Lois: Can you do a quick comparison between hashing and encryption? Orlando: Hashing is one way. It's used to confirm that data hasn't changed. Once data is hashed, it cannot be reversed. It's perfect for use cases like password storage or checking the integrity of files. Encryption, on the other hand, it's two-way. It's designed to protect data from unauthorized access. You encrypt the data so only someone with the right key can decrypt and read it. That's what makes it ideal for keeping files, messages, or network traffic confidential. Both are essential for different reasons. Hashing for trust and encryption for privacy. 07:11 Adopting a multicloud strategy is a big step towards future-proofing your business and we’re here to help you navigate this complex landscape. With our suite of courses, you'll gain insights into network connectivity, security protocols, and the considerations of working across different cloud platforms. Start your journey to multicloud today by visiting mylearn.oracle.com. 07:39 Nikita: Welcome back! When we talk about cybersecurity, we hear a lot about threats and vulnerabilities. But what do those terms really mean? Orlando: In cybersecurity, a threat is a potential danger and a vulnerability is a weakness an asset possess that a threat can exploit. When a threat and a vulnerability align, it creates a risk of harm. A threat actor then performs an exploit to leverage that vulnerability, leading to undesirable impact, such as data loss or downtime. After an impact, the focus shifts to response and recovery to mitigate damage and restore operations. 08:23 Lois: Ok, let’s zero in on vulnerabilities. What counts as a vulnerability, and what categories do attackers usually target first? Orlando: Software and hardware bugs are simply unintended flaws in a system's core programming or design. Misconfigurations arise when systems aren't set up securely, leaving gaps. Weak passwords and authentication provide easy entry points for attackers. A lack of encryption means sensitive data is openly exposed. Human error involves mistakes made by people that unintentionally create security risks. Understanding these common vulnerability types is the first step in building more resilient and secure systems as they represent the critical entry points attackers leverage to compromise systems and data. By addressing these, we can significantly reduce our attack surface and enhance overall security. 09:28 Nikita: Can we get more specific here? What are the most common cybersecurity threats that go after vulnerabilities in our systems and data? Orlando: Malware is a broad category, including viruses, worms, Trojans, and spyware. Its goal is to disrupt or damage systems. Ransomware has been on the rise, targeting everything from hospitals to government agencies. It lock your files and demands a ransom, usually in cryptocurrency. Phishing relies on deception. Attackers impersonate legitimate contacts to trick users into clicking malicious links or giving up credentials. Insider threats are particularly dangerous because they come within employees, contractors, or even former staff with lingering access. Lastly, DDoS attacks aim to make online services unavailable by overwhelming them with traffic, often using a botnet—a network of compromised devices. 10:34 Lois: Orlando, can you walk us through how each of these common cybersecurity threats work? Orlando: Malware, short for malicious software, is one of the oldest and most pervasive types of threats. It comes in many forms, each with unique methods and objectives. A virus typically attaches itself to executable files and documents and spreads when those are shared or opened. Worms are even more dangerous in networked environments as they self-replicate and spread without any user action. Trojans deceive users by posing as harmless or helpful applications. Once inside, they can steal data or open backdoors for remote access. Spyware runs silently in the background, collecting sensitive information like keystrokes or login credentials. Adware might seem like just an annoyance, but it can also track your activity and compromise privacy. Finally, rootkits are among the most dangerous because they operate at a low system level, often evading detection tools and allowing attackers long-term access. In practice, malware can be a combination of these types. Attackers often bundle different techniques to maximize damage. 12:03 Nikita: And what about ransomware? Why it is such a serious threat? Orlando: Ransomware has become one of the most disruptive and costly types of cyber attacks in recent years. Its goal is simple but devastating, to encrypt your data and demand payment in exchange for access. It usually enters through phishing emails, insecure remote desktop protocol ports or known vulnerabilities. Once inside, it often spreads laterally across the network before activating, ensuring maximum impact. There are two common main forms. Crypto ransomware encrypts user files, making them inaccessible. Locker ransomware goes a step further, locking the entire system interface, preventing any use at all. Victims are then presented with a ransom note, typically requesting cryptocurrency payments in exchange for the decryption key. What makes ransomware so dangerous is not just the encryption itself, but the pressure it creates. Healthcare institutions, for instance, can't afford the downtime, making them prime targets. 13:18 Lois: Wow. Thanks, Orlando, for joining us today. Nikita: Yeah, thanks Orlando. We’ll be back next week with more on how you use security models to tackle these threats head-on. And if you want to learn about the topics we covered today, go to mylearn.oracle.com and search for the Cloud Tech Jumpstart course. Until next time, this is Nikita Abraham… Lois: And Lois Houston, signing off! 13:42 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
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Networking & Security Essentials
11/11/2025
Networking & Security Essentials
How do all your devices connect and stay safe in the cloud? In this episode, Lois Houston and Nikita Abraham talk with OCI instructors Sergio Castro and Orlando Gentil about the basics of how networks work and the simple steps that help protect them. You’ll learn how information gets from one place to another, why tools like switches, routers, and firewalls are important, and what goes into keeping access secure. The discussion also covers how organizations decide who can enter their systems and how they keep track of activity. Cloud Tech Jumpstart: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. -------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hi everyone! In the last episode, we spoke about local area networks and domain name systems. Today, we’ll continue our conversation on the fundamentals of networking, covering a variety of important topics. 00:50 Lois: That’s right, Niki. And before we close, we’ll also touch on the basics of security. Joining us today are two OCI instructors from Oracle University: Sergio Castro and Orlando Gentil. So glad to have you both with us guys. Sergio, with so many users and devices connecting to the internet, how do we make sure everyone can get online? Can you break down what Network Address Translation, or NAT, does to help with this? Sergio: The world population is bigger than 4.3 billion people. That means that if we were to interconnect every single human into the internet, we will not have enough addresses. And not all of us are connected to the internet, but those of us who are, you know that we have more than one device at our disposal. We might have a computer, a laptop, mobile phones, you name it. And all of them need IP addresses. So that's why Network Address Translation exists because it translates your communication from a private IP to a public IP address. That's the main purpose: translate. 02:05 Nikita: Okay, so with NAT handling the IP translation, how do we ensure that the right data reaches the right device within a network? Or to put it differently, what directs external traffic to specific devices inside a network? Sergio: Port forwarding works in a reverse way to Network Address Translation. So, let's assume that this PC here, you want to turn it into a web server. So, people from the outside, customers from the outside of your local area network, will access your PC web server. Let's say that it's an online store. Now all of these devices are using the same public IP address. So how would the traffic be routed specifically to this PC and not to the camera or to the laptop, which is not a web server, or to your IP TV? So, this is where port forwarding comes into play. Basically, whenever it detects a request coming to port, it will route it and forward that request to your PC. It will allow anybody, any external device that wants to access this particular one, this particular web server, for the session to be established. So, it's a permission that you're allowing to this PC and only to this PC. The other devices will still be isolated from that list. That's what port forwarding is. 03:36 Lois: Sergio, let’s talk about networking devices. What are some of the key ones, and what role do they play in connecting everything together? Sergio: There's plenty of devices for interconnectivity. These are devices that are different from the actual compute instances, virtual machines, cameras, and IPTV. These are for interconnecting networks. And they have several functionalities. 03:59 Nikita: Yeah, I often hear about a default gateway. Could you explain what that is and why it’s essential for a network to function smoothly? Sergio: A gateway is basically where a web browser goes and asks a service from a web server. We have a gateway in the middle that will take us to that web server. So that's basically is the router. A gateway doesn't necessarily have to be a router. It depends on what device you're addressing at a particular configuration. So, a gateway is a connectivity device that connects two different networks. That's basically the functionality. 04:34 Lois: Ok. And when does one use a default gateway? Sergio: When you do not have a specific route that is targeting a specific router. You might have more than one router in your network, connecting to different other local area networks. You might have a route that will take you to local area network B. And then you might have another router that is connecting you to the internet. So, if you don't have a specific route that will take you to local area network B, then it's going to be utilizing the default gateway. It directs data packets to other networks when no specific route is known. In general terms, the default gateway, again, it doesn't have to be a router. It can be any devices. 05:22 Nikita: Could you give us a real-world example, maybe comparing a few of these devices in action, so we can see how they work together in a typical network? Sergio: For example, we have the hub. And the hub operates at the physical layer or layer 1. And then we have the switch. And the switch operates at layer 2. And we also have the router. And the router operates at layer 3. So, what's the big difference between these devices and the layers that they operate in? So, hubs work in the physical layer of the OSI model. And basically, it is for connecting multiple devices and making them act as a single network segment. Now, the switch operates at the data link layer and is basically a repeater, and is used for filtering content by reading the addresses of the source and destination. And these are the MAC addresses that I'm talking about. So, it reads where the packet is coming from and where is it going to at the local area network level. It connects multiple network segments. And each port is connected to a different segment. And the router is used for routing outside of your local area network, performs traffic directing functions on the internet. A data packet is typically forwarded from one router to another through different networks until it reaches its destination node. The switch connects multiple network segments. And each port of the switch is connected to a different segment. And the router performs traffic directing functions on the internet. It takes data from one router to another, and it works at the TCP/IP network layer or internet layer. 07:22 Lois: Sergio, what kind of devices help secure a network from external threats? Sergio: The network firewall is used as a security device that acts as a barrier between a trusted internal network and an untrusted external network, such as the internet. The network firewall is the first line of defense for traffic that passes in and out of your network. The firewall examines traffic to ensure that it meets the security requirements set by your organization, or allowing, or blocking traffic based on set criteria. And the main benefit is that it improves security for access management and network visibility. 08:10 Are you keen to stay ahead in today's fast-paced world? We’ve got your back! Each quarter, Oracle rolls out game-changing updates to its Fusion Cloud Applications. And to make sure you’re always in the know, we offer New Features courses that give you an insider’s look at all of the latest advancements. Don't miss out! Head over to mylearn.oracle.com to get started. 08:36 Nikita: Welcome back! Sergio, how do networks manage who can and can’t enter based on certain permissions and criteria? Sergio: The access control list is like the gatekeeper into your local area network. Think about the access control list as the visa on your passport, assuming that the country is your local area network. Now, when you have a passport, you might get a visa that allows you to go into a certain country. So the access control list is a list of rules that defines which users, groups, or systems have permissions to access specific resources on your networks. It is a gatekeeper, that is going to specify who's allowed and who's denied. If you don't have a visa to go into a specific country, then you are denied. Similar here, if you are not part of the rule, if the service that you're trying to access is not part of the rules, then you cannot get in. 09:37 Lois: That’s a great analogy, Sergio. Now, let’s turn our attention to one of the core elements of network security: authentication and authorization. Orlando, can you explain why authentication and authorization are such crucial aspects of a secure cloud network? Orlando: Security is one of the most critical pillars in modern IT systems. Whether you are running a small web app or managing global infrastructure, every secure system starts by answering two key questions. Who are you, and what are you allowed to do? This is the essence of authentication and authorization. Authentication is the first step in access control. It's how a system verifies that you are who you claim to be. Think of it like showing your driver's license at a security checkpoint. The guard checks your photo and personal details to confirm your identity. In IT systems, the same process happens using one or more of these factors. It will ask you for something you know, like a password. It will ask you for something that you have, like a security token, or it will ask you for something that you are, like a fingerprint. An identity does not refer to just a person. It's any actor, human or not, that interacts with your systems. Users are straightforward, think employees logging into a dashboard. But services and machines are equally important. A backend API may need to read data from a database, or a virtual machine may need to download updates. Treating these non-human identities with the same rigor as human ones helps prevent unauthorized access and improves visibility and security. After confirming your identity, can the system move on to deciding what you're allowed to access? That's where authorization comes in. Once authentication confirms who you are, authorization determines what you are allowed to do. Sticking with the driver's license analogy, you've shown your license and proven your identity, but that doesn't mean that you can drive anything anywhere. Your license class might let you drive a car, not a motorcycle or a truck. It might be valid in your country, but not in others. Similarly, in IT systems, authorization defines what actions you can take and on which resources. This is usually controlled by policies and roles assigned to your identity. It ensures that users or services only get access to the things they are explicitly allowed to interact with. 12:34 Nikita: How can organizations ensure secure access across their systems, especially when managing multiple users and resources? Orlando: Identity and Access Management governs who can do what in our systems. Individually, authentication verifies identity and authorization grants access. However, managing these processes at scale across countless users and resources becomes a complex challenge. That's where Identity and Access Management, or IAM, comes in. IAM is an overarching framework that centralizes and orchestrates both authentication and authorization, along with other critical functions, to ensure secure and efficient access to resources. 13:23 Lois: And what are the key components and methods that make up a robust IAM system? Orlando: User management, a core component of IAM, provides a centralized Identity Management system for all user accounts and their attributes, ensuring consistency across applications. Key functions include user provisioning and deprovisioning, automating account creation for new users, and timely removal upon departure or role changes. It also covers the full user account lifecycle management, including password policies and account recovery. Lastly, user management often involves directory services integration to unify user information. Access management is about defining access permissions, specifically what actions users can perform and which resources they can access. A common approach is role-based access control, or RBAC, where permissions are assigned to roles and users inherit those permissions by being assigned to roles. For more granular control, policy-based access control allows for rules based on specific attributes. Crucially, access management enforces the principle of least privilege, granting only the minimum necessary access, and supports segregation of duties to prevent conflicts of interest. For authentication, IAM systems support various methods. Single-factor authentication, relying on just one piece of evidence like a password, offers basic security. However, multi-factor authentication significantly boosts security by requiring two or more distinct verification types, such as a password, plus a one-time code. We also have biometric authentication, using unique physical traits and token-based authentication, common for API and web services. 15:33 Lois: Orlando, when it comes to security, it's not just about who can access what, but also about keeping track of it all. How does auditing and reporting maintain compliance? Orlando: Auditing and reporting are essential for security and compliance. This involves tracking user activities, logging all access attempts and permission changes. It's vital for meeting compliance and regulatory requirements, allowing you to generate reports for audits. Auditing also aids in security incident detection by identifying unusual activities and providing data for forensic analysis after an incident. Lastly, it offers performance and usage analytics to help optimize your IAM system. 16:22 Nikita: That was an incredibly informative conversation. Thank you, Sergio and Orlando, for sharing your expertise with us. If you’d like to dive deeper into these concepts, head over to mylearn.oracle.com and search for the Cloud Tech Jumpstart course. Lois: I agree! This was such a great conversation! Don’t miss next week’s episode, where we’ll continue exploring key security concepts to help organizations operate in a scalable, secure, and auditable way. Until next time, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 16:56 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
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Inside Cloud Networking
11/04/2025
Inside Cloud Networking
In this episode, hosts Lois Houston and Nikita Abraham team up with Senior Principal OCI Instructor Sergio Castro to unpack the basics of cloud networking and the Domain Name System (DNS). You’ll learn how local and virtual networks connect devices, and how DNS seamlessly translates familiar names like oracle.com into addresses computers understand. Cloud Tech Jumpstart: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------------ Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hi everyone! For the last few weeks, we’ve been talking about different aspects of cloud data centers. Today, we’re focusing on something that’s absolutely key to how everything works in the cloud: networking and domain name systems. 00:52 Lois: And to guide us through it, we’ve got Sergio Castro, Senior Principal OCI Instructor at Oracle University. We’ll start by trying to understand why networking is so crucial and how it connects everything behind the scenes. Sergio, could you explain what networking means in simple terms, especially for folks new to cloud tech? Sergio: Networking is the backbone of cloud computing. It is a fundamental service because it provides the infrastructure for connecting users, applications, and resources within a cloud environment. It basically enables data transfers. It facilitates remote access. And ensures that cloud services are accessible to users. This provided that these users have the correct credentials. 01:38 Nikita: Ok, can you walk us through how a typical network operates? Sergio: In networking, typically starts with the local area network. Basically, networking is a crucial component for any IT service because it's the foundation for the architecture framework of any of the services that we consume today. So, a network is two or more computers interconnected to each other. And not necessarily it needs to be a computer. It can be another device such as a printer or an IP TV or an IP phone or an IP camera. Many devices can be part of a local area network. And a local area network can be very small. Like I mentioned before, two or more computers, or it could grow into a very robust and complicated set of interconnected networks. And if that happens, then it can become very expensive as well. Cloud networking, it's the Achilles heel for many of the database administrators, programmers, quality assurance engineers, any IT other than a network administrator. Actually, when the network starts to grow, managing access and permissions and implementing robust security measures, this coupled with the critical importance of reliable, and secure performance, can create significant hurdles. 03:09 Nikita: What are the different types of networks we have? Sergio: A local area network is basically in one building. It covers… it can be maybe two buildings that are in close proximity in a small campus, but typically it's very small by definition, and they're all interconnected to each other via one router, typically. A metropolitan area network is a typical network that spans into a city or a metro area, hence the name metropolitan area network. So, one building can be on one edge of the city and the other building can be at the other edge of the city, and they are interconnected by a digital circuit typically. So that's the case. It's more than one building, and the separation of those buildings is considerable. It can go into several miles. And a wide area network is a network that spans multiple cities, states, countries, even international. 04:10 Lois: I think we’ll focus on the local area network for today’s conversation. Could you give us a real-world example, maybe what a home office network setup looks like? Sergio: If you are accessing this session from your home office or from your office or corporate office even, but a home office or a home network, typically, you have a router that is being provided to you by the internet vendor—the internet service provider. And then you have your laptop or your computer, your PC connected to that router. And then you might have other devices either connected via cable—ethernet cable—or Wi-Fi. And the interconnectivity within that small building is what makes a local area network. And it looks very similar once you move on into a corporate office. Again, it's two or more computers interconnected. That's what makes a local area network. In a corporate office, the difference with a home office or your home is that you have many more computers. And because you have many more computers, that local area network might be divided into subnets. And for that, you need a switch. So, you have additional devices like a switch and a firewall and the router. And then you might have a server as well. So that's the local area network. Two or more computers. And local area networks are capable of high speeds because they are in close proximity to each other. 05:47 Nikita: Ok… so obviously a local area network has several different components. Let’s break them down. What’s a client, what’s a server, and how do they interact? Sergio: A client basically is a requester of a service. Like when you hop into your browser and then you want to go to a website, for example, oracle.com, you type www.oracle.com, you are requesting a service from a server. And that server typically resides in a data center like oracle.com under the Oracle domain is a big data center with many interconnected servers. Interconnected so they can concurrently serve multiple millions of requests coming into www.oracle.com at the same time. So, servers provide services to client computers. So basically, that's the relation. A client requests a service and the server provides that service. 06:50 Lois: And what does that client-server setup actually look like? Sergio: So, let's continue with our example of a web browser requesting a service from a web server. So, in this case, the physical computer is the server. And then it has a software running on it. And that makes it a web server. So, once you type www.oracle.com, it sends the request and the request is received. And provided that everything's configured correctly and that there are no typos, then it will provide a response and basically give the view of the website. And that's obviously in the local area network, maybe quality assurance when they were testing this for going live. But when it goes live, then you have the internet in the middle. And the internet in the middle then have many routers, hubs, switches. 07:51 Transform the way you work with Oracle Database 23ai! This cutting-edge technology brings the power of AI directly to your data, making it easier to build powerful applications and manage critical workloads. Want to learn more about Database 23ai? Visit mylearn.oracle.com to pick from our range of courses and enroll today! 08:16 Nikita: Welcome back! Sergio, would this client-server model also apply to my devices at home? Sergio: In your own local area network, you have client server even without noticing. For example, let's go back to our home office example. What happens if we add another laptop into the scenario? Then all of these devices, they need a way for them to communicate. And for that, they have an IP address. And who provides that IP address? The minute that you add, the other device is going to send a request to the router. The router, we call it router, but it has multiple functions like the mobile device, the handheld device that we call smartphone. It has many functions like camera and calendar and many other functionalities. The router has an additional functionality called the dynamic host configuration protocol at DHCP server. So basically, the laptop requests, hey, give me an IP address, and then the router or the DHCP server replies, here's your IP address. And it's going to be a different one. So, they don't overlap. So that's an example of client server. 09:32 Lois: And where do virtual networks fit into all this? Sergio: A virtual network is basically, a software version of the physical network. It looks and feels exactly as a physical network does. We do have a path or a communication, in this case, in the physical network, you have either Wi-Fi or you have internet cable. And then you add your workstations or devices on top of that. And then you might create subnets. So, in a software-defined network or in a virtual network, you have a software-defined connectivity, physical cable and all of that. Everything is software-defined. And it looks exactly the same, except that everything is software. In a software or a virtual network, you can communicate with a physical network as if that software or that virtual network was another physical network. Again, this is a software network or a software-defined network, a virtual network, no longer a physical network. 10:42 Lois: Let’s switch gears a little and talk about Domain Name Systems. Sergio, can you explain what DNS is, and why it’s important when we browse the web? Sergio: DNS is the global database for internet addressing. The DNS plays a very important role on the internet. And many internet services are closely related to DNS. The main functionality of DNS is to translate easy-to-remember names into IP addresses. Some IP addresses might be very easy to remember. But however, if you have many of them, then it's easier to remember oracle.com or ucla.edu or navy.mil for military or eds.org for organization or gobierno.mx for Mexico. So that's the main feature of the DNS. It's very similar to a mobile phone to the contacts application in your mobile phone, because the contacts application maps names to phone numbers. It's easier to remember Bob's phone than 555-123-4567. So, it's easier to remember the name of the persons in your contacts list, like it is easier to remember, as previously mentioned, oracle.com than 138.1.33.162. Again, 138.1.33.162 might be easy for you to remember if that's the only one that you need to remember. But if you have 20, 40, 50, like we do with phone numbers, it's easier to remember oracle.com or ucla.edu. And this is essential, this mapping, again, because we work with names it's easier for us to remember. However, the fact is that computers, they still need to use IP addresses. And remember that this is the decimal representation of the binary number. It's a lot harder for us to remember the 32 bits or each one of the octets in binary. So that's the main purpose of DNS. Now the big difference is that the contact list in a cell phone is unique to that individual phone. However, DNS is global. It applies to everybody in the world. Anybody typing oracle.com will translate that into 138.1.33.162. Now this is an actual IP address of oracle.com. Oracle.com has many IP addresses. If you ping oracle.com, chances are that this is one of the many addresses that maps to oracle.com. 13:35 Nikita: You mentioned that a domain name like oracle.com can have many IP addresses. So how does DNS help my computer find the right one? Sergio: So, let's say that you want to look for www.example.com, how do you do that? So, you type in your computer instance or in your terminal, in your laptop, in your computer, you type in your browser "www.example.com." If the browser doesn't have that information in cache, then it's going to first ask your DNS server, the one that you have assigned and indicating in your browser's configuration. And if the DNS server then it will relate that the information is 96.7.128.198. This address is real, and your browser will go to this address once you type www.example.com. 14:34 Nikita: But what happens if the browser doesn’t know the address? Sergio: This is where it gets interesting. Your browser wants to go to www.example.com. And it's going to go and look within its cache. If it doesn't have it, then the first step is to go ahead to your DNS server and ask them, hey, if you don't know this address, go ahead and find out. So, it goes to the root server. All the servers are administrated by IANA. And it's going to send the information, hey, what's the IP address for www.example.com? And if the root server doesn't know it, it's going to let you know, hey, ask the top-level domain name server, in this case, the .com. It's a top-level domain name server. So, you go ahead and ask this top-level domain name server to do that for you. In this case, again, the .com and you asked, hey, what's the IP address for example.com? And if the top-level domain name server doesn't know, it's going to ask you, hey, ask example.com. And example.com is actually within the customer's domain. And then based on these instructions you ask, what is the IP address for www.example.com? So, it will provide you with the IP address. And once your DNS server has the IP address, then it's going to relate to your web browser. And this is where your web browser actually reaches 96.7.128.198. Very interesting, isn't it? 16:23 Lois: Absolutely! Sergio, you mentioned top-level domain names. What are they and how are they useful? Sergio: A top level domain is the rightmost segment of a domain name, and it's located after the last visible dot in the domain name. So oracle.com or cloud.oracle.com is a domain name. So, .com is a top-level domain. And the purpose of the top-level domain is to recognize certain elements of a website. This top-level domain indicates that this is a commercial site. Now, .edu, for example, is a top-level domain name for higher education. We also have .org for nonprofit organizations, .net for network service providers. And we also have country specific. .ca for Canadian websites, .it for Italian websites. Now .it, a lot of companies that are in the information technology business utilizes this one to indicate that they're in information technology. There's also the .us. And for US companies, most of the time this is optional. .com, .org, .net is understood that they are from the US. Now if .com is a top-level domain name, what is that .oracle in cloud? So, Oracle is the second-level domain name. And in this case, Cloud is the third-level domain name. And lately you've been seeing a lot more top-level domain names. These are the classic ones. But now you get .AI, .media, .comedy, .people, and so on and so forth. You have many, many, even companies now have the option of registering their company name as the top-level domain name. 18:24 Nikita: Thank you, Sergio, for this deep dive into local area networks and domain name systems. If you want to learn about the topics we covered today, go to mylearn.oracle.com and search for the Cloud Tech Jumpstart course. Lois: And don’t forget to join us next week for another episode on networking essentials. Until next time, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 18:46 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
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Cloud Data Centers: Core Concepts - Part 4
10/28/2025
Cloud Data Centers: Core Concepts - Part 4
In this episode, hosts Lois Houston and Nikita Abraham, along with Principal OCI Instructor Orlando Gentil, break down the differences between Infrastructure-as-a-Service, Platform-as-a-Service, and Software-as-a-Service. The conversation explores how each framework influences control, cost efficiency, expansion, reliability, and contingency planning. Cloud Tech Jumpstart: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ----------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Nikita: Welcome to the Oracle University Podcast! I’m Nikita Abraham, Team Lead: Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hey there! Last week, we spoke about how hypervisors, virtual machines, and containers have transformed data centers. Today, we’re moving on to something just as important—the main cloud models that drive modern cloud computing. Nikita: Orlando Gentil, Principal OCI Instructor at Oracle University, joins us once again for part four of our discussion on cloud data centers. 01:01 Lois: Hi Orlando! Glad to have you with us today. Can you walk us through the different types of cloud models? Orlando: These are commonly categorized into three main service models: Infrastructure-as-a-Service, Platform-as-a-Service, and Software-as-a-Service. Let's use the idea of getting around town to understand cloud service models. IaaS is like renting a car. You don't own the car, but you control where it goes, how fast, and when to stop. In cloud terms, the provider gives you the infrastructure—virtual machines, storage, and networking—but you manage everything on top—the OS, middleware, runtime, and application. Thus, it's like using a shuttle service. You bring your bags—your code, pick your destination—your app requirements, but someone else drives and maintains the vehicle. You don't worry about the engine, fuel, or routing planning. That's the platform's job. Your focus stays on development and deployment, not on servers or patching. SaaS is like ordering a taxi. You say where you want to go and everything else is handled for you. It's the full-service experience. In the cloud, SaaS is software UXs over the web—Email, CRM, project management. No infrastructure, no updates, just productivity. 02:32 Nikita: Ok. How do the trade-offs between control and convenience differ across SaaS, PaaS, and IaaS? Orlando: With IaaS, much like renting a car, you gain high control. You are managing components like the operating system, runtime, your applications, and your data. In return, the provider expertly handles the underlying virtual machines, storage, and networking. This model gives you immense flexibility. Moving to PaaS, our shuttle service, you shift to a medium level of control but gain significantly higher convenience. Your primary focus remains on your application code and data. The provider now takes on the heavy lifting of managing the runtime environment, the operating system, the servers themselves, and even the scaling. Finally, SaaS, our taxi service, offers the highest convenience with the lowest control level. Here, your responsibility is essentially just using the application and managing your specific configurations or data within it. The cloud provider manages absolutely everything else—the entire infrastructure, the platform, and the application itself. 03:52 Nikita: One of the top concerns for cloud users is cost optimization. How can we manage this? Orlando: Each cloud service model offers distinct strategies to help you manage and reduce your spending effectively, as well as different factors that drives those costs. For Infrastructure-as-a-Service, where you have more control, optimization largely revolves around smart resource management. This means rightsizing your VMs, ensuring they are not overprovisioned, and actively turning off idle resources when not in use. Leveraging preemptible or spot instances for flexible workloads can also significantly cut costs. Your charges here are directly tied to your compute, storage, and network usage, so efficiency is key. Moving to Platform-as-a-Service, where the platform is managed for you, optimization shifts slightly. Strategies include choosing scalable platforms that can efficiently handle fluctuating demand, opting for consumption-based pricing where available, and diligently optimizing your runtime usage to minimize processing time. Costs in PaaS are typically based on your application usage, runtime hours, and storage consumed. Finally, for Software-as-a-Service where you can consume a ready-to-use application, cost optimization centers on licensing and usage. This involves consolidating tools to avoid redundant subscriptions, selecting usage-based plans if they align better with your needs, and crucially, eliminating any unused license. SaaS costs are generally based on subscription or per user fees. Understanding these nuances is essential for effective cloud financial management. 05:52 Lois: Ok. And what about scalability? How does each model handle the ability to grow and shrink with demand, without needing manual hardware changes? Orlando: How you achieve and manage that scalability varies significantly across our three service models. For Infrastructure-as-a-Service, you have the most direct control over scaling. You can implement manual or auto scaling by adding or removing virtual machines as needed, often leveraging load balancers to distribute traffic. In this model, you configure the scaling policies and parameters based on your specific workload. Moving to Platform-as-a-Service, the scaling becomes more automated and elastic. The platform automatically adjusts resources based on your application's demand, allowing it to seamlessly handle traffic spikes or dips. Here, the provider manages the underlying scaling behavior, freeing you from that operational burden. Finally, with Software-as-a-Service, scalability is largely abstracted and invisible to the user. The application scales automatically in the background, with the entire process fully managed by the provider. As a user, you simply benefit from the application's ability to handle millions of users without ever needing to worry about the infrastructure. Understanding these scaling differences is crucial for selecting the right model for your application's need. 07:34 Join the Oracle University Learning Community and tap into a vibrant network of over 1 million members, including Oracle experts and fellow learners. This dynamic community is the perfect place to grow your skills, connect with likeminded learners, and celebrate your successes. As a MyLearn subscriber, you have access to engage with your fellow learners and participate in activities in the community. Visit community.oracle.com/ou to check things out today! 08:05 Nikita: Welcome back! We’ve talked about cost optimization and scalability in cloud environments. But what about ensuring availability? How does that work? Orlando: Availability refers to the ability of a system or service to remain accessible in operational, even in the face of failures or extremely high demand. The approach of achieving and managing availability, and crucially, your role versus the provider's differs greatly across each model. With Infrastructure-as-a-Service, you have the most direct control over your availability strategy. You will be responsible for designing an architecture that includes redundant VMs, deploying load balancers, and potentially even multi-region setups for disaster recovery. Your specific roles involves designing this architecture and managing your failover process and data backups. The provider’s role, in turn, is to deliver the underlying infrastructure with defined service level agreements, SLAs, and health monitoring. For Platform-as-a-Service, the platform itself offers a higher degree of built-in, high availability, and automated failover. While the provider maintains the runtime platform’s availability, your role shifts. You need to ensure your application's logic is designed to gracefully handle retries and potential transient failures that might occur. Finally, with Software-as-a-Service, availability is almost entirely handled for you. The provider ensures fully abstracted redundancy and failover behind the scenes. Your role becomes largely minimal, often just involving a specific application’s configurations. The provider is entirely responsible for the full application uptime and the underlying high availability infrastructure. Understanding these distinct roles in ensuring availability is essential for setting expectations and designing your cloud strategy efficiently. 10:19 Lois: Building on availability, let’s talk Disaster Recovery. Orlando: DR is about ensuring your systems and data can be recovered and brought back online in the event of a significant failure, whether it's a hardware crash, a natural disaster, or even human error. Just like the other aspects, the strategy and responsibilities for DR vary significantly across the cloud service models. For Infrastructure-as-a Service, you have the most direct involvement in your DR strategy. You need to design and execute custom DR plans. This involves leveraging capabilities like multi-region backups, taking VM snapshots, and setting up failover clusters. A real-world example might be using Oracle Cloud compute to replicate your VMs to a secondary region with block volume backups to ensure business continuity. Essentially, you manage your entire DR process here. Moving to Platform-as-a-Service, disaster recovery becomes a shared responsibility. The platform itself offers built-in redundancy and provide APIs for backup and restore. Your role will be to configure the application-level recovery and ensure your data is backed up appropriately, while the provider handles the underlying infrastructure's DR capability. An example could be Azure app service, Oracle APEX applications, where your apps are redeployed from source control like Git after an incident. Finally, with Software-as-a-Service, disaster recovery is almost entirely vendor managed. The provider takes full responsibility, offering features like auto replication and continuous backup, often backed by specific Recovery Point Objective (RPO) and Recovery Time Objective (RTO) SLAs. A common example is how Microsoft 365 or Salesforce manage user data backups in restoration. It's all handled seamlessly by the provider without your direct intervention. Understanding these different approaches to DR is crucial for defining your own business continuity plans in the cloud. 12:46 Lois: Thank you, Orlando, for this insightful discussion. To recap, we spoke about the three main cloud models: IaaS, PaaS, and SaaS, and how each one offers a different mix of control and convenience, impacting cost, scalability, availability, and recovery. Nikita: Yeah, hopefully this helps you pick the right cloud solution for your needs. If you want to learn more about the topics we discussed today, head over to mylearn.oracle.com and search for the Cloud Tech Jumpstart course. In our next episode, we’ll take a close look at the essentials of networking. Until then, this is Nikita Abraham… Lois: And Lois Houston, signing off! 13:26 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
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Cloud Data Centers: Core Concepts - Part 3
10/21/2025
Cloud Data Centers: Core Concepts - Part 3
Have you ever considered how a single server can support countless applications and workloads at once? In this episode, hosts Lois Houston and Nikita Abraham, together with Principal OCI Instructor Orlando Gentil, explore the sophisticated technologies that make this possible in modern cloud data centers. They discuss the roles of hypervisors, virtual machines, and containers, explaining how these innovations enable efficient resource sharing, robust security, and greater flexibility for organizations. Cloud Tech Jumpstart: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. -------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hi everyone! For the last two weeks, we’ve been talking about different aspects of cloud data centers. In this episode, Orlando Gentil, Principal OCI Instructor at Oracle University, joins us once again to discuss how virtualization, through hypervisors, virtual machines, and containers, has transformed data centers. 00:58 Lois: That’s right, Niki. We’ll begin with a quick look at the history of virtualization and why it became so widely adopted. Orlando, what can you tell us about that? Orlando: To truly grasp the power of virtualization, it's helpful to understand its journey from its humble beginnings with mainframes to its pivotal role in today's cloud computing landscape. It might surprise you, but virtualization isn't a new concept. Its roots go back to the 1960s with mainframes. In those early days, the primary goal was to isolate workloads on a single powerful mainframe, allowing different applications to run without interfering with each other. As we moved into the 1990s, the challenge shifted to underutilized physical servers. Organizations often had numerous dedicated servers, each running a single application, leading to significant waste of computing resources. This led to the emergence of virtualization as we know it today, primarily from the 1990s to the 2000s. The core idea here was to run multiple isolated operating systems on a single physical server. This innovation dramatically improved the resource utilization and laid the technical foundation for cloud computing, enabling the scalable and flexible environments we rely on today. 02:26 Nikita: Interesting. So, from an economic standpoint, what pushed traditional data centers to change and opened the door to virtualization? Orlando: In the past, running applications often meant running them on dedicated physical servers. This led to a few significant challenges. First, more hardware purchases. Every new application, every new project often required its own dedicated server. This meant constantly buying new physical hardware, which quickly escalated capital expenditure. Secondly, and hand-in-hand with more servers came higher power and cooling costs. Each physical server consumed power and generated heat, necessitating significant investment in electricity and cooling infrastructure. The more servers, the higher these operational expenses became. And finally, a major problem was unused capacity. Despite investing heavily in these physical servers, it was common for them to run well below their full capacity. Applications typically didn't need 100% of server's resources all the time. This meant we were wasting valuable compute power, memory, and storage, effectively wasting resources and diminishing the return of investment from those expensive hardware purchases. These economic pressures became a powerful incentive to find more efficient ways to utilize data center resources, setting the stage for technologies like virtualization. 04:05 Lois: I guess we can assume virtualization emerged as a financial game-changer. So, what kind of economic efficiencies did virtualization bring to the table? Orlando: From a CapEx or capital expenditure perspective, companies spent less on servers and data center expansion. From an OpEx or operational expenditure perspective, fewer machines meant lower electricity, cooling, and maintenance costs. It also sped up provisioning. Spinning a new VM took minutes, not days or weeks. That improved agility and reduced the operational workload on IT teams. It also created a more scalable, cost-efficient foundation which made virtualization not just a technical improvement, but a financial turning point for data centers. This economic efficiency is exactly what cloud providers like Oracle Cloud Infrastructure are built on, using virtualization to deliver scalable pay as you go infrastructure. 05:09 Nikita: Ok, Orlando. Let’s get into the core components of virtualization. To start, what exactly is a hypervisor? Orlando: A hypervisor is a piece of software, firmware, or hardware that creates and runs virtual machines, also known as VMs. Its core function is to allow multiple virtual machines to run concurrently on a single physical host server. It acts as virtualization layer, abstracting the physical hardware resources like CPU, memory, and storage, and allocating them to each virtual machine as needed, ensuring they can operate independently and securely. 05:49 Lois: And are there types of hypervisors? Orlando: There are two primary types of hypervisors. The type 1 hypervisors, often called bare metal hypervisors, run directly on the host server's hardware. This means they interact directly with the physical resources offering high performance and security. Examples include VMware ESXi, Oracle VM Server, and KVM on Linux. They are commonly used in enterprise data centers and cloud environments. In contrast, type 2 hypervisors, also known as hosted hypervisors, run on top of an existing operating system like Windows or macOS. They act as an application within that operating system. Popular examples include VirtualBox, VMware Workstation, and Parallels. These are typically used for personal computing or development purposes, where you might run multiple operating systems on your laptop or desktop. 06:55 Nikita: We’ve spoken about the foundation provided by hypervisors. So, can we now talk about the virtual entities they manage: virtual machines? What exactly is a virtual machine and what are its fundamental characteristics? Orlando: A virtual machine is essentially a software-based virtual computer system that runs on a physical host computer. The magic happens with the hypervisor. The hypervisor's job is to create and manage these virtual environments, abstracting the physical hardware so that multiple VMs can share the same underlying resources without interfering with each other. Each VM operates like a completely independent computer with its own operating system and applications. 07:40 Lois: What are the benefits of this? Orlando: Each VM is isolated from the others. If one VM crashes or encounters an issue, it doesn't affect the other VMs running on the same physical host. This greatly enhances stability and security. A powerful feature is the ability to run different operating systems side-by-side on the very same physical host. You could have a Windows VM, a Linux VM, and even other specialized OS, all operating simultaneously. Consolidate workloads directly addresses the unused capacity problem. Instead of one application per physical server, you can now run multiple workloads, each in its own VM on a single powerful physical server. This dramatically improves hardware utilization, reducing the need of constant new hardware purchases and lowering power and cooling costs. And by consolidating workloads, virtualization makes it possible for cloud providers to dynamically create and manage vast pools of computing resources. This allows users to quickly provision and scale virtual servers on demand, tapping into these shared pools of CPU, memory, and storage as needed, rather than being tied to a single physical machine. 09:10 Oracle University’s Race to Certification 2025 is your ticket to free training and certification in today’s hottest technology. Whether you’re starting with Artificial Intelligence, Oracle Cloud Infrastructure, Multicloud, or Oracle Data Platform, this challenge covers it all! Learn more about your chance to win prizes and see your name on the Leaderboard by visiting education.oracle.com/race-to-certification-2025. That’s education.oracle.com/race-to-certification-2025. 09:54 Nikita: Welcome back! Orlando, let’s move on to containers. Many see them as a lighter, more agile way to build and run applications. What’s your take? Orlando: A container packages an application in all its dependencies, like libraries and other binaries, into a single, lightweight executable unit. Unlike a VM, a container shares the host operating system's kernel, running on top of the container runtime process. This architectural difference provides several key advantages. Containers are incredibly portable. They can be taken virtually anywhere, from a developer's laptop to a cloud environment, and run consistently, eliminating it works on my machine issues. Because containers share the host OS kernel, they don't need to bundle a full operating system themselves. This results in significantly smaller footprints and less administration overhead compared to VMs. They are faster to start. Without the need to boot a full operating system, containers can start up in seconds, or even milliseconds, providing rapid deployment and scaling capabilities. 11:12 Nikita: Ok. Throughout our conversation, you’ve spoken about the various advantages of virtualization but let’s consolidate them now. Orlando: From a security standpoint, virtualization offers several crucial benefits. Each VM operates in its own isolated sandbox. This means if one VM experiences a security breach, the impact is generally contained to that single virtual machine, significantly limiting the spread of potential threats across your infrastructure. Containers also provide some isolation. Virtualization allows for rapid recovery. This is invaluable for disaster recovery or undoing changes after a security incident. You can implement separate firewalls, access rules, and network configuration for each VM. This granular control reduces the overall exposure and attack surface across your virtualized environments, making it harder for malicious actors to move laterally. Beyond security, virtualization also brings significant advantages in terms of operational and agility benefits for IT management. Virtualization dramatically improves operational efficiency and agility. Things are faster. With virtualization, you can provision new servers or containers in minutes rather than days or weeks. This speed allows for quicker deployment of applications and services. It becomes much simpler to deploy consistent environment using templates and preconfigured VM images or containers. This reduces errors and ensures uniformity across your infrastructure. It's more scalable. Virtualization makes your infrastructure far more scalable. You can reshape VMs and containers to meet changing demands, ensuring your resources align precisely with your needs. These operational benefits directly contribute to the power of cloud computing, especially when we consider virtualization's role in enabling cloud and scalability. Virtualization is the very backbone of modern cloud computing, fundamentally enabling its scalability. It allows multiple virtual machines to run on a single physical server, maximizing hardware utilization, which is essential for cloud providers. This capability is core of infrastructure as a service offerings, where users can provision virtualized compute resources on demand. Virtualization makes services globally scalable. Resources can be easily deployed and managed across different geographic regions to meet worldwide demand. Finally, it provides elasticity, meaning resources can be automatically scaled up or down in response to fluctuating workloads, ensuring optimal performance and cost efficiency. 14:21 Lois: That’s amazing. Thank you, Orlando, for joining us once again. Nikita: Yeah, and remember, if you want to learn more about the topics we covered today, go to mylearn.oracle.com and search for the Cloud Tech Jumpstart course. Lois: Well, that’s all we have for today. Until next time, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 14:40 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
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Cloud Data Centers: Core Concepts - Part 2
10/14/2025
Cloud Data Centers: Core Concepts - Part 2
Have you ever wondered where all your digital memories, work projects, or favorite photos actually live in the cloud? In this episode, Lois Houston and Nikita Abraham are joined by Principal OCI Instructor Orlando Gentil to discuss cloud storage. They explore how data is carefully organized, the different ways it can be stored, and what keeps it safe and easy to find. Cloud Tech Jumpstart: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------------------ Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Nikita: Welcome to the Oracle University Podcast! I’m Nikita Abraham, Team Lead of Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hey there! Last week, we spoke about the differences between traditional and cloud data centers, and covered components like CPU, RAM, and operating systems. If you haven’t listened to the episode yet, I’d suggest going back and listening to it before you dive into this one. Nikita: Joining us again is Orlando Gentil, Principal OCI Instructor at Oracle University, and we’re going to ask him about another fundamental concept: storage. 01:04 Lois: That’s right, Niki. Hi Orlando! Thanks for being with us again today. You introduced cloud data centers last week, but tell us, how is data stored and accessed in these centers? Orlando: At a fundamental level, storage is where your data resides persistently. Data stored on a storage device is accessed by the CPU and, for specialized tasks, the GPU. The RAM acts as a high-speed intermediary, temporarily holding data that the CPU and the GPU are actively working on. This cyclical flow ensures that applications can effectively retrieve, process, and store information, forming the backbone for our computing operations in the data center. 01:52 Nikita: But how is data organized and controlled on disks? Orlando: To effectively store and manage data on physical disks, a structured approach is required, which is defined by file systems and permissions. The process began with disks. These are the raw physical storage devices. Before data can be written to them, disks are typically divided into partitions. A partition is a logical division of a physical disk that acts as if it were a separated physical disk. This allows you to organize your storage space and even install multiple operating systems on a single drive. Once partitions are created, they are formatted with a file system. 02:40 Nikita: Ok, sorry but I have to stop you there. Can you explain what a file system is? And how is data organized using a file system? Orlando: The file system is the method and the data structure that an operating system uses to organize and manage files on storage devices. It dictates how data is named, is stored, retrieved, and managed on the disk, essentially providing the roadmap for data. Common file systems include NTFS for Windows and ext4 or XFS for Linux. Within this file system, data is organized hierarchically into directories, also known as folders. These containers help to logically group related files, which are the individual units of data, whether they are documents, images, videos, or applications. Finally, overseeing this entire organization are permissions. 03:42 Lois: And what are permissions? Orlando: Permissions define who can access a specific files and directories and what actions they are allowed to perform-- for example, read, write, or execute. This access control, often managed by user, group, and other permissions, is fundamental for security, data integrity, and multi-user environments within a data center. 04:09 Lois: Ok, now that we have a good understanding of how data is organized logically, can we talk about how data is stored locally within a server? Orlando: Local storage refers to storage devices directly attached to a server or computer. The three common types are Hard Disk Drive. These are traditional storage devices using spinning platters to store data. They offer large capacity at a lower cost per gigabyte, making them suitable for bulk data storage when high performance isn't the top priority. Unlike hard disks, solid state drives use flash memory to store data, similar to USB drives but on a larger scale. They provide significantly faster read and write speeds, better durability, and lower power consumption than hard disks, making them ideal for operating systems, applications, and frequently accessed data. Non-Volatile Memory Express is a communication interface specifically designed for solid state that connects directly to the PCI Express bus. NVME offers even faster performance than traditional SATA-based solid state drives by reducing latency and increasing bandwidth, making it the top choice for demanding workloads that require extreme speed, such as high-performance databases and AI applications. Each type serves different performance and cost requirements within a data center. While local storage is essential for immediate access, data center also heavily rely on storage that isn't directly attached to a single server. 05:59 Lois: I’m guessing you’re hinting at remote storage. Can you tell us more about that, Orlando? Orlando: Remote storage refers to data storage solutions that are not physically connected to the server or client accessing them. Instead, they are accessed over the network. This setup allows multiple clients or servers to share access to the same storage resources, centralizing data management and improving data availability. This architecture is fundamental to cloud computing, enabling vast pools of shared storage that can be dynamically provisioned to various users and applications. 06:35 Lois: Let’s talk about the common forms of remote storage. Can you run us through them? Orlando: One of the most common and accessible forms of remote storage is Network Attached Storage or NAS. NAS is a dedicated file storage device connected to a network that allows multiple users and client devices to retrieve data from a centralized disk capacity. It's essentially a server dedicated to serving files. A client connects to the NAS over the network. And the NAS then provides access to files and folders. NAS devices are ideal for scenarios requiring shared file access, such as document collaboration, centralized backups, or serving media files, making them very popular in both home and enterprise environments. While NAS provides file-level access over a network, some applications, especially those requiring high performance and direct block level access to storage, need a different approach. 07:38 Nikita: And what might this approach be? Orlando: Internet Small Computer System Interface, which provides block-level storage over an IP network. iSCSI or Internet Small Computer System Interface is a standard that allows the iSCSI protocol traditionally used for local storage to be sent over IP networks. Essentially, it enables servers to access storage devices as if they were directly attached even though they are located remotely on the network. This means it can leverage standard ethernet infrastructure, making it a cost-effective solution for creating high performance, centralized storage accessible over an existing network. It's particularly useful for server virtualization and database environments where block-level access is preferred. While iSCSI provides block-level access over standard IP, for environments demanding even higher performance, lower latency, and greater dedicated throughput, a specialized network is often deployed. 08:47 Nikita: And what’s this specialized network called? Orlando: Storage Area Network or SAN. A Storage Area Network or SAN is a high-speed network specifically designed to provide block-level access to consolidated shared storage. Unlike NAS, which provides file level access, a SAN presents a storage volumes to servers as if they were local disks, allowing for very high performance for applications like databases and virtualized environments. While iSCSI SANs use ethernet, many high-performance SANs utilize fiber channel for even faster and more reliable data transfer, making them a cornerstone of enterprise data centers where performance and availability are paramount. 09:42 Oracle University’s Race to Certification 2025 is your ticket to free training and certification in today’s hottest technology. Whether you’re starting with Artificial Intelligence, Oracle Cloud Infrastructure, Multicloud, or Oracle Data Platform, this challenge covers it all! Learn more about your chance to win prizes and see your name on the Leaderboard by visiting education.oracle.com/race-to-certification-2025. That’s education.oracle.com/race-to-certification-2025. 10:26 Nikita: Welcome back! Orlando, are there any other popular storage paradigms we should know about? Orlando: Beyond file level and block level storage, cloud environments have popularized another flexible and highly scalable storage paradigm, object storage. Object storage is a modern approach to storing data, treating each piece of data as a distinct, self-contained unit called an object. Unlike file systems that organize data in a hierarchy or block storage that breaks data into fixed size blocks, object storage manages data as flat, unstructured objects. Each object is stored with unique identifiers and rich metadata, making it highly scalable and flexible for massive amounts of data. This service handles the complexity of storage, providing access to vast repositories of data. Object storage is ideal for use cases like cloud-native applications, big data analytics, content distribution, and large-scale backups thanks to its immense scalability, durability, and cost effectiveness. While object storage is excellent for frequently accessed data in rapidly growing data sets, sometimes data needs to be retained for very long periods but is accessed infrequently. For these scenarios, a specialized low-cost storage tier, known as archive storage, comes into play. 12:02 Lois: And what’s that exactly? Orlando: Archive storage is specifically designed for long-term backup and retention of data that you rarely, if ever, access. This includes critical information, like old records, compliance data that needs to be kept for regulatory reasons, or disaster recovery backups. The key characteristics of archive storage are extremely low cost per gigabyte, achieved by optimizing for infrequent access rather than speed. Historically, tape backup systems were the common solution for archiving, where data from a data center is moved to tape. In modern cloud environments, this has evolved into cloud backup solutions. Cloud-based archiving leverages high-cost, effective during cloud storage tiers that are purpose built for long term retention, providing a scalable and often more reliable alternative to physical tapes. 13:05 Lois: Thank you, Orlando, for taking the time to talk to us about the hardware and software layers of cloud data centers. This information will surely help our listeners to make informed decisions about cloud infrastructure to meet their workload needs in terms of performance, scalability, cost, and management. Nikita: That’s right, Lois. And if you want to learn more about what we discussed today, head over to mylearn.oracle.com and search for the Cloud Tech Jumpstart course. Lois: In our next episode, we’ll take a look at more of the fundamental concepts within modern cloud environments, such as Hypervisors, Virtualization, and more. I can’t wait to learn more about it. Until then, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 13:47 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
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Cloud Data Centers: Core Concepts - Part 1
10/07/2025
Cloud Data Centers: Core Concepts - Part 1
Curious about what really goes on inside a cloud data center? In this episode, Lois Houston and Nikita Abraham chat with Principal OCI Instructor Orlando Gentil about how cloud data centers are transforming the way organizations manage technology. They explore the differences between traditional and cloud data centers, the roles of CPUs, GPUs, and RAM, and why operating systems and remote access matter more than ever. Cloud Tech Jumpstart: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hi everyone! Today, we’re covering the fundamentals you need to be successful in a cloud environment. If you’re new to cloud, coming from a SaaS environment, or planning to move from on-premises to the cloud, you won’t want to miss this. With us today is Orlando Gentil, Principal OCI Instructor at Oracle University. Hi Orlando! Thanks for joining us. 01:01 Lois: So Orlando, we know that Oracle has been a pioneer of cloud technologies and has been pivotal in shaping modern cloud data centers, which are different from traditional data centers. For our listeners who might be new to this, could you tell us what a traditional data center is? Orlando: A traditional data center is a physical facility that houses an organization's mission critical IT infrastructure, including servers, storage systems, and networking equipment, all managed on site. 01:32 Nikita: So why would anyone want to use a cloud data center? Orlando: The traditional model requires significant upfront investment in physical hardware, which you are then responsible for maintaining along with the underlying infrastructure like physical security, HVAC, backup power, and communication links. In contrast, cloud data centers offer a more agile approach. You essentially rent the infrastructure you need, paying only for what you use. In the traditional data center, scaling resources up and down can be a slow and complex process. On cloud data centers, scaling is automated and elastic, allowing resources to adjust dynamically based on demand. This shift allows business to move their focus from the constant upkeep of infrastructure to innovation and growth. The move represents a shift from maintenance to momentum, enabling optimized costs and efficient scaling. This fundamental shift is how IT infrastructure is managed and consumed, and precisely what we mean by moving to the cloud. 02:39 Lois: So, when we talk about moving to the cloud, what does it really mean for businesses today? Orlando: Moving to the cloud represents the strategic transition from managing your own on-premise hardware and software to leveraging internet-based computing services provided by a third-party. This involves migrating your applications, data, and IT operations to a cloud environment. This transition typically aims to reduce operational overhead, increase flexibility, and enhance scalability, allowing organizations to focus more on their core business functions. 03:17 Nikita: Orlando, what’s the “brain” behind all this technology? Orlando: A CPU or Central Processing Unit is the primary component that performs most of the processing inside the computer or server. It performs calculations handling the complex mathematics and logic that drive all applications and software. It processes instructions, running tasks, and operations in the background that are essential for any application. A CPU is critical for performance, as it directly impacts the overall speed and efficiency of the data center. It also manages system activities, coordinating user input, various application tasks, and the flow of data throughout the system. Ultimately, the CPU drives data center workloads from basic server operations to powering cutting edge AI applications. 04:10 Lois: To better understand how a CPU achieves these functions and processes information so efficiently, I think it’s important for us to grasp its fundamental architecture. Can you briefly explain the fundamental architecture of a CPU, Orlando? Orlando: When discussing CPUs, you will often hear about sockets, cores, and threads. A socket refers to the physical connection on the motherboard where a CPU chip is installed. A single server motherboard can have one or more sockets, each holding a CPU. A core is an independent processing unit within a CPU. Modern CPUs often have multiple cores, enabling them to handle several instructions simultaneously, thus increasing processing power. Think of it as having multiple mini CPUs on a single chip. Threads are virtual components that allow a single CPU core to handle multiple sequence of instructions or threads concurrently. This technology, often called hyperthreading, makes a single core appear as two logical processors to the operating system, further enhancing efficiency. 05:27 Lois: Ok. And how do CPUs process commands? Orlando: Beyond these internal components, CPUs are also designed based on different instruction set architectures which dictate how they process commands. CPU architectures are primarily categorized in two designs-- Complex Instruction Set Computer or CISC and Reduced Instruction Set Computer or RISC. CISC processors are designed to execute complex instructions in a single step, which can reduce the number of instructions needed for a task, but often leads to a higher power consumption. These are commonly found in traditional Intel and AMD CPUs. In contrast, RISC processors use a simpler, more streamlined set of instructions. While this might require more steps for a complex task, each step is faster and more energy efficient. This architecture is prevalent in ARM-based CPUs. 06:34 Are you looking to boost your expertise in enterprise AI? Check out the Oracle AI Agent Studio for Fusion Applications Developers course and professional certification—now available through Oracle University. This course helps you build, customize, and deploy AI Agents for Fusion HCM, SCM, and CX, with hands-on labs and real-world case studies. Ready to set yourself apart with in-demand skills and a professional credential? Learn more and get started today! Visit mylearn.oracle.com for more details. 07:09 Nikita: Welcome back! We were discussing CISC and RISC processors. So Orlando, where are they typically deployed? Are there any specific computing environments and use cases where they excel? Orlando: On the CISC side, you will find them powering enterprise virtualization and server workloads, such as bare metal hypervisors in large databases where complex instructions can be efficiently processed. High performance computing that includes demanding simulations, intricate analysis, and many traditional machine learning systems. Enterprise software suites and business applications like ERP, CRM, and other complex enterprise systems that benefit from fewer steps per instruction. Conversely, RISC architectures are often preferred for cloud-native workloads such as Kubernetes clusters, where simpler, faster instructions and energy efficiency are paramount for distributed computing. Mobile device management and edge computing, including cell phones and IoT devices where power efficiency and compact design are critical. Cost optimized cloud hosting supporting distributed workloads where the cumulative energy savings and simpler design lead to more economical operations. The choice between CISC and RISC depends heavily on the specific workload and performance requirements. While CPUs are versatile generalists, handling a broad range of tasks, modern data centers also heavily rely on another crucial processing unit for specialized workloads. 08:54 Lois: We’ve spoken a lot about CPUs, but our conversation would be incomplete without understanding what a Graphics Processing Unit is and why it’s important. What can you tell us about GPUs, Orlando? Orlando: A GPU or Graphics Processing Unit is distinct from a CPU. While the CPU is a generalist excelling at sequential processing and managing a wide variety of tasks, the GPU is a specialist. It is designed specifically for parallel compute heavy tasks. This means it can perform many calculations simultaneously, making it incredibly efficient for workloads like rendering graphics, scientific simulations, and especially in areas like machine learning and artificial intelligence, where massive parallel computation is required. In the modern data center, GPUs are increasingly vital for accelerating these specialized, data intensive workloads. 09:58 Nikita: Besides the CPU and GPU, there’s another key component that collaborates with these processors to facilitate efficient data access. What role does Random Access Memory play in all of this? Orlando: The core function of RAM is to provide faster access to information in use. Imagine your computer or server needing to retrieve data from a long-term storage device, like a hard drive. This process can be relatively slow. RAM acts as a temporary high-speed buffer. When your CPU or GPU needs data, it first checks RAM. If the data is there, it can be accessed almost instantaneously, significantly speeding up operations. This rapid access to frequently used data and programming instructions is what allows applications to run smoothly and systems to respond quickly, making RAM a critical factor in overall data center performance. While RAM provides quick access to active data, it's volatile, meaning data is lost when power is off, or persistent data storage, the information that needs to remain available even after a system shut down. 11:14 Nikita: Let’s now talk about operating systems in cloud data centers and how they help everything run smoothly. Orlando, can you give us a quick refresher on what an operating system is, and why it is important for computing devices? Orlando: At its core, an operating system, or OS, is the fundamental software that manages all the hardware and software resources on a computer. Think of it as a central nervous system that allows everything else to function. It performs several critical tasks, including managing memory, deciding which programs get access to memory and when, managing processes, allocating CPU time to different tasks and applications, managing files, organizing data on storage devices, handling input and output, facilitate communication between the computer and its peripherals, like keyboards, mice, and displays. And perhaps, most importantly, it provides the user interface that allows us to interact with the computer. 12:19 Lois: Can you give us a few examples of common operating systems? Orlando: Common operating system examples you are likely familiar with include Microsoft Windows and MacOS for personal computers, iOS and Android for mobile devices, and various distributions of Linux, which are incredibly prevalent in servers and increasingly in cloud environments. 12:41 Lois: And how are these operating systems specifically utilized within the demanding environment of cloud data centers? Orlando: The two dominant operating systems in data centers are Linux and Windows. Linux is further categorized into enterprise distributions, such as Oracle Linux or SUSE Linux Enterprise Server, which offer commercial support and stability, and community distributions, like Ubuntu and CentOS, which are developed and maintained by communities and are often free to use. On the other side, we have Windows, primarily represented by Windows Server, which is Microsoft's server operating system known for its robust features and integration with other Microsoft products. While both Linux and Windows are powerful operating systems, their licensing modes can differ significantly, which is a crucial factor to consider when deploying them in a data center environment. 13:43 Nikita: In what way do the licensing models differ? Orlando: When we talk about licensing, the differences between Linux and Windows become quite apparent. For Linux, Enterprise Distributions come with associated support fees, which can be bundled into the initial cost or priced separately. These fees provide access to professional support and updates. On the other hand, Community Distributions are typically free of charge, with some providers offering basic community-driven support. Windows server, in contrast, is a commercial product. Its license cost is generally included in the instance cost when using cloud providers or purchased directly for on-premise deployments. It's also worth noting that some cloud providers offer a bring your own license, or BYOL program, allowing organizations to use their existing Windows licenses in the cloud, which can sometimes provide cost efficiencies. 14:46 Nikita: Beyond choosing an operating system, are there any other important aspects of data center management? Orlando: Another critical aspect of data center management is how you remotely access and interact with your servers. Remote access is fundamental for managing servers in a data center, as you are rarely physically sitting in front of them. The two primary methods that we use are SSH, or secure shell, and RDP, remote desktop. Secure shell is widely used for secure command line access for Linux servers. It provides an encrypted connection, allowing you to execute commands, transfer files, and manage your servers securely from a remote location. The remote desktop protocol is predominantly used for graphical remote access to Windows servers. RDP allows you to see and interact with the server's desktop interface, just as if you were sitting directly in front of it, making it ideal for tasks that require a graphical user interface. 15:54 Lois: Thank you so much, Orlando, for shedding light on this topic. Nikita: Yeah, that's a wrap for today! To learn more about what we discussed, head over to mylearn.oracle.com and search for the Cloud Tech Jumpstart course. In our next episode, we’ll take a close look at how data is stored and managed. Until then, this is Nikita Abraham… Lois: And Lois Houston, signing off! 16:16 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
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AI Across Industries and the Importance of Responsible AI
09/30/2025
AI Across Industries and the Importance of Responsible AI
AI is reshaping industries at a rapid pace, but as its influence grows, so do the ethical concerns that come with it. This episode examines how AI is being applied across sectors such as healthcare, finance, and retail, while also exploring the crucial issue of ensuring that these technologies align with human values. In this conversation, Lois Houston and Nikita Abraham are joined by Hemant Gahankari, Senior Principal OCI Instructor, who emphasizes the importance of fairness, inclusivity, transparency, and accountability in AI systems. AI for You: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ---------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hey everyone! In our last episode, we spoke about how Oracle integrates AI capabilities into its Fusion Applications to enhance business workflows, and we focused on Predictive, Generative, and Agentic AI. Lois: Today, we’ll discuss the various applications of AI. This is the final episode in our AI series, and before we close, we’ll also touch upon ethical and responsible AI. 01:01 Nikita: Taking us through all of this is Senior Principal OCI Instructor Hemant Gahankari. Hi Hemant! AI is pretty much everywhere today. So, can you explain how it is being used in industries like retail, hospitality, health care, and so on? Hemant: AI isn't just for sci-fi movies anymore. It's helping doctors spot diseases earlier and even discover new drugs faster. Imagine an AI that can look at an X-ray and say, hey, there is something sketchy here before a human even notices. Wild, right? Banks and fintech companies are all over AI. Fraud detection. AI has got it covered. Those robo advisors managing your investments? That's AI too. Ever noticed how e-commerce companies always seem to know what you want? That's AI studying your habits and nudging you towards that next purchase or binge watch. Factories are getting smarter. AI predicts when machines will fail so they can fix them before everything grinds to a halt. Less downtime, more efficiency. Everyone wins. Farming has gone high tech. Drones and AI analyze crops, optimize water use, and even help with harvesting. Self-driving cars get all the hype, but even your everyday GPS uses AI to dodge traffic jams. And if AI can save me from sitting in bumper-to-bumper traffic, I'm all for it. 02:40 Nikita: Agreed! Thanks for that overview, but let’s get into specific scenarios within each industry. Hemant: Let us take a scenario in the retail industry-- a retail clothing line with dozens of brick-and-mortar stores. Maintaining proper inventory levels in stores and regional warehouses is critical for retailers. In this low-margin business, being out of a popular product is especially challenging during sales and promotions. Managers want to delight shoppers and increase sales but without overbuying. That's where AI steps in. The retailer has multiple information sources, ranging from point-of-sale terminals to warehouse inventory systems. This data can be used to train a forecasting model that can make predictions, such as demand increase due to a holiday or planned marketing promotion, and determine the time required to acquire and distribute the extra inventory. Most ERP-based forecasting systems can produce sophisticated reports. A generative AI report writer goes further, creating custom plain-language summaries of these reports tailored for each store, instructing managers about how to maximize sales of well-stocked items while mitigating possible shortages. 04:11 Lois: Ok. How is AI being used in the hospitality sector, Hemant? Hemant: Let us take an example of a hotel chain that depends on positive ratings on social media and review websites. One common challenge they face is keeping track of online reviews, leading to missed opportunities to engage unhappy customers complaining on social media. Hotel managers don't know what's being said fast enough to address problems in real-time. Here, AI can be used to create a large data set from the tens of thousands of previously published online reviews. A textual language AI system can perform a sentiment analysis across the data to determine a baseline that can be periodically re-evaluated to spot trends. Data scientists could also build a model that correlates these textual messages and their sentiments against specific hotel locations and other factors, such as weather. Generative AI can extract valuable suggestions and insights from both positive and negative comments. 05:27 Nikita: That’s great. And what about Financial Services? I know banks use AI quite often to detect fraud. Hemant: Unfortunately, fraud can creep into any part of a bank's retail operations. Fraud can happen with online transactions, from a phone or browser, and offsite ATMs too. Without trust, banks won't have customers or shareholders. Excessive fraud and delays in detecting it can violate financial industry regulations. Fraud detection combines AI technologies, such as computer vision to interpret scanned documents, document verification to authenticate IDs like driver's licenses, and machine learning to analyze patterns. These tools work together to assess the risk of fraud in each transaction within seconds. When the system detects a high risk, it triggers automated responses, such as placing holds on withdrawals or requesting additional identification from customers, to prevent fraudulent activity and protect both the business and its client. 06:42 Nikita: Wow, interesting. And how is AI being used in the health industry, especially when it comes to improving patient care? Hemant: Medical appointments can be frustrating for everyone involved—patients, receptionists, nurses, and physicians. There are many time-consuming steps, including scheduling, checking in, interactions with the doctors, checking out, and follow-ups. AI can fix this problem through electronic health records to analyze lab results, paper forms, scans, and structured data, summarizing insights for doctors with the latest research and patient history. This helps practice reduced costs, boost earnings, and deliver faster, more personalized care. 07:32 Lois: Let’s take a look at one more industry. How is manufacturing using AI? Hemant: A factory that makes metal parts and other products use both visual inspections and electronic means to monitor product quality. A part that fails to meet the requirements may be reworked or repurposed, or it may need to be scrapped. The factory seeks to maximize profits and throughput by shipping as much good material as possible, while minimizing waste by detecting and handling defects early. The way AI can help here is with the quality assurance process, which creates X-ray images. This data can be interpreted by computer vision, which can learn to identify cracks and other weak spots, after being trained on a large data set. In addition, problematic or ambiguous data can be highlighted for human inspectors. 08:36 Oracle University’s Race to Certification 2025 is your ticket to free training and certification in today’s hottest tech. Whether you’re starting with Artificial Intelligence, Oracle Cloud Infrastructure, Multicloud, or Oracle Data Platform, this challenge covers it all! Learn more about your chance to win prizes and see your name on the Leaderboard by visiting education.oracle.com/race-to-certification-2025. That’s education.oracle.com/race-to-certification-2025. 09:20 Nikita: Welcome back! AI can be used effectively to automate a variety of tasks to improve productivity, efficiency, cost savings. But I’m sure AI has its constraints too, right? Can you talk about what happens if AI isn’t able to echo human ethics? Hemant: AI can fail due to lack of ethics. AI can spot patterns, not make moral calls. It doesn't feel guilt, understand context, or take responsibility. That is still up to us. Decisions are only as good as the data behind them. For example, health care AI underdiagnosing women because research data was mostly male. Artificial narrow intelligence tends to automate discrimination at scale. Recruiting AI downgraded resumes just because it had a word "women's" (for example, women's chess club). Who is responsible when AI fails? For example, if a self-driving car hits someone, we cannot blame the car. Then who owns the failure? The programmer? The CEO? Can we really trust corporations or governments having programmed the use of AI not to be evil correctly? So, it's clear that AI needs oversight to function smoothly. 10:48 Lois: So, Hemant, how can we design AI in ways that respect and reflect human values? Hemant: Think of ethics like a tree. It needs all parts working together. Roots represent intent. That is our values and principles. The trunk stands for safeguards, our systems, and structures. And the branches are the outcomes we aim for. If the roots are shallow, the tree falls. If the trunk is weak, damage seeps through. The health of roots and trunk shapes the strength of our ethical outcomes. Fairness means nothing without ethical intent behind it. For example, a bank promotes its loan algorithm as fair. But it uses zip codes in decision-making, effectively penalizing people based on race. That's not fairness. That's harm disguised as data. Inclusivity depends on the intent sustainability. Inclusive design isn't just a check box. It needs a long-term commitment. For example, controllers for gamers with disabilities are only possible because of sustained R&D and intentional design choices. Without investment in inclusion, accessibility is left behind. Transparency depends on the safeguard robustness. Transparency is only useful if the system is secure and resilient. For example, a medical AI may be explainable, but if it is vulnerable to hacking, transparency won't matter. Accountability depends on the safeguard privacy and traceability. You can't hold people accountable if there is no trail to follow. For example, after a fatal self-driving car crash, deleted system logs meant no one could be held responsible. Without auditability, accountability collapses. So remember, outcomes are what we see, but they rely on intent to guide priorities and safeguards to support execution. That's why humans must have a final say. AI has no grasp of ethics, but we do. 13:16 Nikita: So, what you’re saying is ethical intent and robust AI safeguards need to go hand in hand if we are to truly leverage AI we can trust. Hemant: When it comes to AI, preventing harm is a must. Take self-driving cars, for example. Keeping pedestrians safe is absolutely critical, which means the technology has to be rock solid and reliable. At the same time, fairness and inclusivity can't be overlooked. If an AI system used for hiring learns from biased past data, say, mostly male candidates being hired, it can end up repeating those biases, shutting out qualified candidates unfairly. Transparency and accountability go hand in hand. Imagine a loan rejection if the AI's decision isn't clear or explainable. It becomes impossible for someone to challenge or understand why they were turned down. And of course, robustness supports fairness too. Loan approval systems need strong security to prevent attacks that could manipulate decisions and undermine trust. We must build AI that reflects human values and has safeguards. This makes sure that AI is fair, inclusive, transparent, and accountable. 14:44 Lois: Before we wrap, can you talk about why AI can fail? Let’s continue with your analogy of the tree. Can you explain how AI failures occur and how we can address them? Hemant: Root elements like do not harm and sustainability are fundamental to ethical AI development. When these roots fail, the consequences can be serious. For example, a clear failure of do not harm is AI-powered surveillance tools misused by authoritarian regimes. This happens because there were no ethical constraints guiding how the technology was deployed. The solution is clear-- implement strong ethical use policies and conduct human rights impact assessment to prevent such misuse. On the sustainability front, training AI models can consume massive amount of energy. This failure occurs because environmental costs are not considered. To fix this, organizations are adopting carbon-aware computing practices to minimize AI's environmental footprint. By addressing these root failures, we can ensure AI is developed and used responsibly with respect for human rights and the planet. An example of a robustness failure can be a chatbot hallucinating nonexistent legal precedence used in court filings. This could be due to training on unverified internet data and no fact-checking layer. This can be fixed by grounding in authoritative databases. An example of a privacy failure can be AI facial recognition database created without user consent. The reason being no consent was taken for data collection. This can be fixed by adopting privacy-preserving techniques. An example of a fairness failure can be generated images of CEOs as white men and nurses as women, minorities. The reason being training on imbalanced internet images reflecting societal stereotypes. And the fix is to use diverse set of images. 17:18 Lois: I think this would be incomplete if we don’t talk about inclusivity, transparency, and accountability failures. How can they be addressed, Hemant? Hemant: An example of an inclusivity failure can be a voice assistant not understanding accents. The reason being training data lacked diversity. And the fix is to use inclusive data. An example of a transparency and accountability failure can be teachers could not challenge AI-generated performance scores due to opaque calculations. The reason being no explainability tools are used. The fix being high-impact AI needs human review pathways and explainability built in. 18:04 Lois: Thank you, Hemant, for a fantastic conversation. We got some great insights into responsible and ethical AI. Nikita: Thank you, Hemant! If you’re interested in learning more about the topics we discussed today, head over to mylearn.oracle.com and search for the AI for You course. Until next time, this is Nikita Abraham…. Lois: And Lois Houston, signing off! 18:26 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
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Oracle AI for Fusion Apps
09/23/2025
Oracle AI for Fusion Apps
Want to make AI work for your business? In today’s episode, Lois Houston and Nikita Abraham continue their discussion of AI in Oracle Fusion Applications by focusing on three key AI capabilities: predictive, generative, and agentic. Joining them is Principal Instructor Yunus Mohammed, who explains how predictive, generative, and agentic AI can optimize efficiency, support decision-making, and automate tasks—all without requiring technical expertise. AI for You: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------------------------ Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Nikita: Welcome to the Oracle University Podcast! I’m Nikita Abraham, Team Lead: Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi there! In our last episode, we explored the essential components of the Oracle AI stack and spoke about Oracle’s suite of AI services. Nikita: Yeah, and in today’s episode, we’re going to go down a similar path and take a closer look at the AI functionalities within Oracle Fusion Applications. 00:53 Lois: With us today is Principal Instructor Yunus Mohammed. Hi Yunus! It’s lovely to have you back with us. For anyone who doesn’t already know, what are Oracle Fusion Cloud Applications? Yunus: Oracle Fusion Applications are a suite of cloud-based enterprise applications designed to run for your business across finance, HR, supply chain, sales, services and more, all on a unified platform. They are designed to help enterprises operate smarter, faster by embedding AI directly into business process. That means better forecasts in finance, faster hiring decisions in HR, and optimized supply chains, and more personalized customer experience. 01:42 Nikita: And we know they’ve been built for today's fast-paced, AI-driven business environment. So, what are the different functional pillars within Oracle Fusion Apps? Yunus: The first one is the ERP, Enterprise Resource Planning, which supports financials, procurements, and project management. It's the backbone of many organizations, or day-to-day operations. HCM or Human Capital Management, handles workforce-related processes such as hiring, payroll, performance, and talent development, helping HR teams operate more efficiently. SCM, the Supply Chain Management, enables businesses to manage their logistics, inventory, and suppliers and manufacturers in the business. It's particularly critical in industries with complex operations like retail and manufacturing. The CX, which is the Customer Experience, covers the full customer life cycle, which includes sales, marketing, and service. These models help the businesses connect with their customers more personally and proactively, whether through the targeted campaigns or responsive support. 03:02 Lois: Yunus, what sets Fusion apart? Yunus: What sets Fusion apart is how these applications work seamlessly together. They share data natively and continuously improve with AI and automation, giving you not just tools, but intelligence at scale. Oracle applications are built to be AI first, with a complete suite of finance, supply chain, manufacturing, HR, sales, service, and marketing, that is tightly coupled with our industry and data intelligence applications. The easiest and the most effective way to start building your organization’s AI muscle is with AI embedded in Fusion applications. For example, if the customer needs to return a defective product, the service representative simply clicks on Ask Oracle for the answers. Since the AI agent is embedded in the application, it has contextual information about the customer, the order, and any special service, contract, or any other feature that is required for this process. The AI agent automatically figures out the return policy, including the options to send a replacement product immediately or offer a discount for the inconvenience, and also expedite shipping. Another AI agent sends a personalized email confirming details of the return, and different AI agent creates the replacement order for fulfillment and shipping. Our AI-embedded Fusion Applications can automate an end-to-end business process from service request to return order to fulfillment and shipping and then accounting. These are pre-built and tested so that all the worry and hard work is removed from the implementation point of view. They cover the core workflows. Basically, they address tasks that form part of the organization's core workflow. User requires no technical knowledge in the scenarios. 05:16 Lois: That’s great! So, you don’t need to be an AI expert or a data scientist to get going. Yunus: The outcomes are super fast in business softwares and context is everything. Just having the right information isn't enough. This is about having the information in the right place at the right time for it to be instantly actionable. They are ready from day one and can be optimized over time. They are powerful out of the box and only get better with day-to-day processes and performance. 05:55 Are you working towards an Oracle Certification this year? Join us at one of our certification prep live events in the Oracle University Learning Community. Get insider tips from seasoned experts and learn from others who have already taken their certifications. Go to community.oracle.com/ou to jump-start your journey towards certification today! 06:20 Nikita: Welcome back! So, when we talk about the AI capabilities in Fusion apps, I know we have different types. Can you tell us more about them? Yunus: Predictive AI is where it all started. These models analyze historical patterns and data to anticipate what might happen next. For example, predicting employee attrition, forecasting demand in supply chain, or flagging potential late payments in finance workflows. These are embedded into business processes to surface insights before action is needed. Then we have got the generative AI, which takes this a step more further. Instead of just providing insights, it creates content, such as auto-generating job descriptions, summarizing performance reviews, or even crafting draft responses to supplier queries. This saves time and boosts productivity across functions like HR, CX, and procurement. Last but not the least, we have got the agentic AI, which is the most advanced layer. These agents don't just provide suggestions, they take actions on behalf of the users. Think of an agent that not only recommends actions in a workflow, but also executes them, creating tasks, filling tickets, updating systems, and communicating with stakeholders, all autonomously but under user control. And importantly, many business scenarios today benefit from a blend of these types. For example, an AI assistant in Fusion HCM might predict employees turnover, which is predictive AI, generates tailored retention plans, which is generative, and it is generative AI, and initiate outreach or next steps, which is done by the process of agents, which is called agentic AI. So, Oracle integrates these capabilities in a harmonious way, enabling users to act faster, personalize at scale, and drive better business outcomes. 08:39 Lois: Ok, let’s get into the specifics. How does Oracle use predictive AI across its Fusion apps, helping businesses anticipate what’s coming and act proactively. Yunus: So in HCM, things like recommended jobs, in this, candidates visiting a potential employer’s website encountered an improved online experience, whereby if they have uploaded their resumes, they will be shown job opportunities that match their skills and experience mix. This helps candidates who are unsure what to search by showing them roles and titles they may not have considered. Time to hire provides an estimated as to how long it will take for an HR team to fill an open role, but this is really useful not only in terms of planning, recruitment, but also in terms of understanding whether you might need some temporary cover and for how long will it actually take the process to complete. In the process of supply chain management, the predictive AI is leveraged to revolutionize transit time and estimated time of arrival, which is called as the predictive analysis, enhancing efficiency, and optimizing operations. It can flag abnormal patterns in supply or inventory. For example, if a batch of parts is behaving differently in the production line and predict future demands, helping avoid overstocking or stockouts is a process that can be done by using the SCM predictive analysis or predictive AI. In ERPs, where you can audit your expenses, plan for future expenses, and do dynamic discounting for vendors who are likely to accept earlier payments or earlier payment discounts, it can also speed up reimbursements by automated expense entries. In CX, you have the options to go with adaptive intelligence for sales, which helps representatives prioritize the leads and the likelihood that a specific lead will close, helping representatives focus their time and effort. So predictive scheduling and routing in service delivery ensures that the right resource is assigned to the right customer at the right time, boosting operational efficiency and customer satisfaction, also known as fatigue analysis. 11:23 Lois: Now let’s shift our focus to generative AI. How does Oracle implement generative AI across HCM, ERP, Supply Chain, and CX? Yunus: So, in HCM, the generative AI can automatically generate performance review summaries from raw data, saving time for HR teams, and can help you in providing candidates with summaries of their interview process, feedback, and next steps, all auto generated. With AI assistance, goal creation for employees can be automated, and the system analyzes performance data and trends to propose meaningful and attainable goals, aligning them with organizational objectives and employee capabilities. In SCM, similarly, the generative AI process helps you in defining drafting summaries of purchase orders. So generative AI can automatically create clear, readable synopses, and can be summarized with complex negotiations and discussions, making it easier for supply chain managers to analyze supplier proposals, track negotiations, processes, and understand key takeaways. With predictive AI embedded, it is helping you to leverage to help generate the repairs of master definitions of summaries, and can generate descriptions for item based on their specification, helping product teams automatically generate catalog contents. With ERPs, you can automate the creation of business reports, offering more insights and actionable narratives, rather than just showing the raw data. The AI can provide context, interpretations, and recommendations. AI can also take raw project data and generate a comprehensive, easy-to-read project status, reports that stakeholders can quickly review. In CX, we have got service request summarization, which can provide these long summaries for the customer services and the tickets that have been requested by the customers, allowing support teams to understand the key points in the fraction of time, and can also create knowledge base articles directly from common service requests or inquiries, which not only improves internal knowledge management but also empowers customers by enabling self-service. So generative AI can automatically generate success stories or case studies from successful opportunities or sales, which can be used as marketing content or for internal knowledge sharing. 14:20 Nikita: And what about Oracle's Agentic AI? What are its capabilities across the different pillars? Yunus: In HCM, Agentic AI handles the end-to-end onboarding experience, from explaining policies to guiding document submissions, even booking orientation sessions, allowing the HR staff to focus on human engagement. It can further support HR teams during performance review cycles by surfacing high potential employees, pulling in performance data, and recommending next actions like promotions or learning paths. It helps manage time with requests by checking eligibility, policy constraints, and suggesting appropriate substitutes, reducing administrative frictions and errors. With SCM, the Agentic AI Fusion Applications act as a real time Assistant to ensure buyers follow procurement policies, and reducing compliance risk and manual errors. It can also support sales representatives with real-time insights and next best actions during the quoting or ordering process, improving customer satisfaction and sales performance. With ERP, you can handle document intake, extraction, and routing, saving significant time on manual document management across financial functions using Fusion Applications. AI automates reconciliation tasks by matching transactions, flagging anomalies, and suggesting resolutions. It helps you in reducing close cycle timelines and continuously analyzes profit margins. And it recommends the pricing adjustments that can be done in your ERPs. In CX, the Agentic AI Fusion Application supports staff by instantly compiling full customer histories, orders, service requests, interactions, and can act like a real-time assistant, summarizing open tickets and resolutions, helping agents take over or escalate without needing to dig through the notes, and dynamically adjust technicals and technician routes based on traffic, priority, or cancelation, increasing the field efficiency and customer satisfaction. 17:04 Lois: Thank you so much, Yunus. To learn more about the topics covered today, visit mylearn.oracle.com and search for the AI for You course. Nikita: Join us next week as we cover how AI is being applied across sectors like healthcare, finance, and retail, and tackle the big question: how do we keep these technologies aligned with human values? Until then, this is Nikita Abraham… Lois: And Lois Houston, signing off! 17:30 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
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Oracle's AI Ecosystem
09/16/2025
Oracle's AI Ecosystem
In this episode, Lois Houston and Nikita Abraham are joined by Principal Instructor Yunus Mohammed to explore Oracle’s approach to enterprise AI. The conversation covers the essential components of the Oracle AI stack and how each part, from the foundational infrastructure to business-specific applications, can be leveraged to support AI-driven initiatives. They also delve into Oracle’s suite of AI services, including generative AI, language processing, and image recognition. AI for You: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hey everyone! In our last episode, we discussed why the decision to buy or build matters in the world of AI deployment. Lois: That’s right, Niki. Today is all about the Oracle AI stack and how it empowers not just developers and data scientists, but everyday business users as well. Then we’ll spend some time exploring Oracle AI services in detail. 01:00 Nikita: Yunus Mohammed, our Principal Instructor, is back with us today. Hi Yunus! Can you talk about the different layers in Oracle’s end-to-end AI approach? Yunus: The first base layer is the foundation of AI infrastructure, the powerful compute and storage layer that enables scalable model training and inferences. Sitting above the infrastructure, we have got the data platform. This is where data is stored, cleaned, and managed. Without a reliable data foundation, AI simply can't perform. So base of AI is the data, and the reliable data gives more support to the AI to perform its job. Then, we have AI and ML services. These provide ready-to-use tools for building, training, and deploying custom machine learning models. Next, to the AI/ML services, we have got generative AI services. This is where Oracle enables advanced language models and agentic AI tools that can generate content, summarize documents, or assist users through chat interfaces. Then, we have the top layer, which is called as the applications, things like Fusion applications or industry specific solutions where AI is embedded directly into business workflows for recommendations, forecasting or customer support. Finally, Oracle integrates with a growing ecosystem of AI partners, allowing organizations to extend and enhance their AI capabilities even further. In short, Oracle doesn't just offer AI as a feature. It delivers it as a full stack capability from infrastructure to the layer of applications. 02:59 Nikita: Ok, I want to get into the core AI services offered by Oracle Cloud Infrastructure. But before we get into the finer details, broadly speaking, how do these services help businesses? Yunus: These services make AI accessible, secure, and scalable, enabling businesses to embed intelligence into workflows, improve efficiency, and reduce human effort in repetitive or data-heavy tasks. And the best part is, Oracle makes it easy to consume these through application interfaces, APIs, software development kits like SDKs, and integration with Fusion Applications. So, you can add AI where it matters without needing a data scientist team to do that work. 03:52 Lois: So, let’s get down to it. The first core service is Oracle's Generative AI service. What can you tell us about it? Yunus: This is a fully managed service that allows businesses to tap into the power of large language models. You can actually work with these models from scratch to a well-defined develop model. You can use these models for a wide range of use cases like summarizing text, generating content, answering questions, or building AI-powered chat interfaces. 04:27 Lois: So, what will I find on the OCI Generative AI Console? Yunus: OCI Generative AI Console highlights three key components. The first one is the dedicated AI cluster. These are GPU powered environments used to fine tune and host your own custom models. It gives you control and performance at scale. Then, the second point is the custom models. You can take a base language model and fine tune it using your own data, for example, company manuals or HR policies or customer interactions, which are your own personal data. You can use this to create a model that speaks your business language. And last but not the least, the endpoints. These are the interfaces through which your application connect to the model. Once deployed, your app can query the model securely and at different scales, and you don't need to be a developer to get started. Oracle offers a playground, which is a non-core environment where you can try out models, craft parameters, and test responses interactively. So overall, the generative AI service is designed to make enterprise-grade AI accessible and customizable. So, fitting directly into business processes, whether you are building a smart assistant or you're automating the content generation process. 06:00 Lois: The next key service is OCI Generative AI Agents. Can you tell us more about it? Yunus: OCI Generative AI agents combines a natural language interface with generative AI models and enterprise data stores to answer questions and take actions. The agent remembers the context, uses previous interactions, and retrieves deeper product speech details. They aren't just static chat bots. They are context aware, grounded in business data, and able to handle multi-turns, follow-up queries with relevant accurate responses, and driving productivity and decision-making across departments like sales, support, or operations. 06:54 Oracle University’s Race to Certification 2025 is your ticket to free training and certification in today’s hottest tech. Whether you’re starting with Artificial Intelligence, Oracle Cloud Infrastructure, Multicloud, or Oracle Data Platform, this challenge covers it all! Learn more about your chance to win prizes and see your name on the Leaderboard by visiting education.oracle.com/race-to-certification-2025. That’s education.oracle.com/race-to-certification-2025. 07:37 Nikita: Welcome back! Yunus, let’s move on to the OCI Language service. Yunus: OCI Language helps business understand and process natural language at scale. It uses pretrained models, which means they are already trained on large industry data sets and are ready to be used right away without requiring AI expertise. It detects over 100 languages, including English, Japanese, Spanish, and more. This is great for global business that receive multilingual inputs from customers. It works with identity sentiments. For different aspects of the sentence, for example, in a review like, “The food was great, but the service sucked,” OCI Language can tell that food has a positive sentiment while service has a negative one. This is called aspect-based sentiment analysis, and it is more insightful than just labeling the entire text as positive or negative. Then we have got to identify key phrases representing important ideas or subjects. So, it helps in extracting these key phrases, words, or terms that capture the core messages. They help automate tagging, summarizing, or even routing of content like support tickets or emails. In real life, the businesses are using this for customer feedback analysis, support ticket routing, social media monitoring, and even regulatory compliances. 09:21 Nikita: That’s fantastic. And what about the OCI Speech service? Yunus: The OCI Speech is an AI service that transcribes speech to text. Think of it as an AI-powered transcription engine that listens to the spoken English, whether in audio or video files, and turns it into usable and searchable and readable text. It provides timestamps, so you know exactly when something was said. A valuable feature for reviewing legal discussions, media footages, or compliance audits. OCI Speech even understands different speakers. You don't need to train this from scratch. It is pre-trained model hosted on an API. Just send your audio to the service, and you get an accurate timestamp text back in return. 10:17 Lois: I know we also have a service for object detection… called OCI Vision? Yunus: OCI Vision uses pretrained, deep learning models to understand and analyze visual content. Just like a human might, you can upload an image or videos, and the AI can tell you what is in it and where they might be useful. There are two primary use cases, which you can use this particular OCI Vision for. One is for object detection. You have got a red color car. So OCI Vision is not just identifying that’s a car. It is detecting and labeling parts of the car too, like the bumper, the wheels, the design components. This is a critical in industries like manufacturing, retail, or logistics. For example, in quality control, OCI Vision can scan product images to detect missing or defective parts automatically. Then we have got the image classification. This is useful in scenarios like automated tagging of photos, managing digital assets, classifying this particular scene or context of this particular scene. So basically, when we talk about OCI Vision, which is actually a fully managed, no complex model training is required for this particular service. It's available via API. It is also working with defining their own custom model for working with the environments. 11:51 Nikita: And the final service is related to text and called OCI Document Understanding, right? Yunus: So OCI Document Understanding allows businesses to automatically extract structured insights from unstructured documents like invoices, contracts, recipes, and also sometimes resumes, or even business documents. 12:13 Nikita: And how does it work? Yunus: OCI reads the content from the scanned document. The OCR is smarter. It recognizes both printed and handwritten text. Then determines what type of document it is. So document classification is done. Text recognition recognizes text, then classifies the document. For example, if this is a purchase order, or bank statement, or any medical report. If your business handles documents in multiple languages, then the AI can actually help in language detection also, which helps you in routing the language or translating that particular language. Many documents contain structured data in table format. Think pricing tables or line items. OCI will help you in extracting these with high accuracy for reporting on feeding into ERP systems. And finally, I would say the key value extraction. It puts our critical business values like invoice numbers, payment amounts, or customer names from fields that may not always allow a fixed format. So, this service reduces the need for manual review, cuts down processes time, and ensures high accuracy for your system. 13:36 Lois: What are the key takeaways our listeners should walk away with after this episode? Yunus: The first one, Oracle doesn't treat AI as just a standalone tool. Instead, AI is integrated from the ground up. Whether you're talking about infrastructure, data platforms, machine learning services, or applications like HCM, ERP, or CX. In real world, the Oracle AI Services prioritize data management, security, and governance, all essential for enterprise AI use cases. So, it is about trust. Can your AI handle sensitive data? Can it comply with regulations? Oracle builds its AI services with strong foundation in data governance, robust security measures, and tight control over data residency and access. So this makes Oracle AI especially well-suited for industries like health care, finance, logistics, and government, where compliance and control aren't optional. They are critical. 14:44 Nikita: Thank you for another great conversation, Yunus. If you’re interested in learning more about the topics we discussed today, head on over to mylearn.oracle.com and search for the AI for You course. Lois: In our next episode, we’ll get into Predictive AI, Generative AI, Agentic AI, all with respect to Oracle Fusion Applications. Until then, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 15:10 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
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Buy or Build AI?
09/09/2025
Buy or Build AI?
How do you decide whether to buy a ready-made AI solution or build one from the ground up? The choice is more than just a technical decision; it’s about aligning AI with your business goals. In this episode, Lois Houston and Nikita Abraham are joined by Principal Instructor Yunus Mohammed to examine the critical factors influencing the buy vs. build debate. They explore real-world examples where businesses must weigh speed, customization, and long-term strategy. From a startup using a SaaS chatbot to a bank developing a custom fraud detection model, Yunus provides practical insights on when to choose one approach over the other. AI for You: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. --------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:26 Nikita: Welcome to the Oracle University Podcast! I’m Nikita Abraham, Team Lead: Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi there! Last week, we spoke about the key stages in a typical AI workflow and how data quality, feedback loops, and business goals influence AI success. 00:50 Nikita: In today’s episode, we’re going to explore whether you should buy or build AI apps. Joining us again is Principal Instructor Yunus Mohammed. Hi Yunus, let’s jump right in. Why does the decision of buy versus build matter? Yunus: So when we talk about buy versus build matters, we need to consider the strategic business decisions over here. They are related to the strategic decisions which the business makes, and it is evaluated in the decision lens. So the center of the decision lens is the business objective, which identifies what are we trying to solve. Then evaluate our constraints based on that particular business objective like the cost, the time, and the talent. And finally, we can decide whether we need to buy or build. But remember, there is no single correct answer. What's right for one business may not be working for the other one. 01:54 Lois: OK, can you give us examples of both approaches? Yunus: The first example where we have got a startup using a SaaS AI chatbot. Now, being a startup, they have to choose a ready-made solution, which is an AI chatbot. Now, the question is, why did they do this? Because speed and simplicity mattered more than deep customization that is required for the chatbot. So, their main aim was to have it ready in short period of time and make it more simpler. And this actually lead them to get to the market fast with low upfront cost and minimal technical complexities. But in some situations, it might be different. Like, your bank, which needs to build a fraud model. It cannot be outsourced or got from the shelf. So, they build a custom model in-house. With this custom model, they actually have a tighter control, and it is tuned to their standards. And it is created by their experts. So these two generic examples, the chatbot and the fraud model example, helps you in identifying whether I should go for a SaaS product with simple choice of selecting an existing LLM endpoint and not making any changes. Or should I go with model depending on my business and organization requirement and fine tuning that model later to define a better implementation of the scenarios or conditions that I want to do which are specific to my organization. So here you decide with the reference whether I want it to be done faster, or whether I want to be more customized to my organization. So buy it, when it is generic, or build when it is strategic. The SaaS, which is basically software as a service, refers to ready to use cloud-based applications that you access via internet. You can log into the platform and use the built-in AI, there's no setup requirement for those. Real-world examples can be Oracle Fusion apps with AI features enabled. So in-house integration means embedding AI with my own requirements into your own systems, often using custom APIs, data pipelines, and hosting it. It gives you more flexibility but requires a lot of resources and expertise. So real-world example for this scenario can be a logistics heavy company, which is integrating a customer support model into their CX. 04:41 Lois: But what are the pros and cons of each approach? Yunus: So, SaaS and Fusion Applications, basically, they offer fast deployment with little to no coding required, making them ideal for business looking to get started quickly and faster. And they typically come with lower upfront costs and are maintained by vendor, which means updates, security, support are handled externally. However, there are limited customizations and are best suited for common, repeatable use cases. Like, it can be a standard chatbot, or it can be reporting tools, or off the shelf analytics that you want to use. But the in-house or custom integration, you have more control, but it takes longer to build and requires a higher initial investment. The in-house or custom integration approach allows full customization of the features and the workflows, enabling you to design and tailor the AI system to your specific needs. 05:47 Nikita: If you're weighing the choice between buying or building, what are the critical business considerations you'd need to take into account? Yunus: So let's take one of the key business consideration which is time to market. If your goal is to launch fast, maybe you're a startup trying to gain traction quickly, then a prebuilt plug and play AI solution, for example, a chatbot or any other standard analytical tool, might be your best bet. But if you have time and you are aiming for precision, a custom model could be worth the wait. Prebuilt SaaS tools usually have lower upfront costs and a subscription model. It works with putting subscriptions. Custom solutions, on the other hand, may require a bigger investment upfront. In development, you require high talent and infrastructures, but could offer cost savings in the long run. So, ask yourself a question here. Is this AI helping us stand out in the market? If the answer is yes, you may want to build something which is your proprietary. For example, an organization would use a generic recommendation engine. It's a part of their secret sauce. Some use cases require flexibility, like you want to tailor the rules to match your specific risk criteria. So, under that scenarios, you will go for customizing. So, you will go with off the shelf solutions may not give you deep enough requirements that you want to evaluate. So, you get those and you try to customize those. You can go for customization of your AI features. The other important key business consideration is the talent and expertise that your organization have. So, the question that you need to ask in the organization is, do you have an internal team who is well versed in developing AI solutions for you? Or do you have access to one of the teams which can help you build your own proprietary products? If not, you'll go with SaaS. If you do have, then building could unlock greater control over your AI features and AI models. The next core component is your security and data privacy. If you're handling sensitive information, like for example, the health care or finance data, you might not want to send your data to the third-party tools. So in-house models offer better control over data security and compliance. When we leverage a model, it could be a prebuilt or custom model. 08:50 Oracle University is proud to announce three brand new courses that will help your teams unlock the power of Redwood—the next generation design system. Redwood enhances the user experience, boosts efficiency, and ensures consistency across Oracle Fusion Cloud Applications. Whether you're a functional lead, configuration consultant, administrator, developer, or IT support analyst, these courses will introduce you to the Redwood philosophy and its business impact. They’ll also teach you how to use Visual Builder Studio to personalize and extend your Fusion environment. Get started today by visiting mylearn.oracle.com. 09:31 Nikita: Welcome back! So, getting back to what you were saying before the break, what are pre-built and custom models? Yunus: A prebuilt model is an AI solution that has already been trained by someone else, typically a tech provider. It can be used to perform a specific task like recognizing images, translating text, or detecting sentiments. You can think of it like buying a preassembled appliance. You plug it in, configure a few settings, and it's ready to use. You don't need to know how the internal parts work. You benefit from the speed, ease, and reliability of this particular model, which is a prebuilt model. But you can't easily change how it works under the hood. Whereas, a custom model is an AI solution that your organization designs and trains and tunes specifically for their business problems using their own data. You can think of it like designing your own suit. It takes more time and effort to create. It is built to your exact measurements and needs. And you have full control over how it performs and evolves. 10:53 Lois: So, when would you choose a pre-built versus a custom model? Yunus: Depending on speed, simplicity, control, and customization, you can decide on using a prebuilt or to create a custom model. Prebuilt models are like plug and play solutions. Think of tools like Google Translate for languages. OpenAI APIs for summarizing sentiment analysis or chatbots, they are quick to deploy, require low technical effort, great for getting started fast, but they also have limits. Customization is very minimal, and you may not be able to fine tune it to your specific tone or business logic. These work well when the problem is common and nonstrategic, like, scanning documents or auto tagging images. The custom-build model, on the other hand, is a model that is built from the ground up. Using your own data and objectives, they take longer, and they require technical expertise. But they offer precise control, full alignment with your business needs. And these are ideal when you are dealing with sensitive data, competitive workflows, highly specific customer interactions. For example, a bank may build a custom model which can be used for fraud detection, which can be tuned to their exact transaction standards and the patterns of their transactions. 12:37 Nikita: What if someone wants the best of both worlds? Yunus: We've also got a hybrid approach. In hybrid approach, we actually talk about the adaptation of AI with a strategy which is termed as hybrid strategy. Many companies today don't start by building AI from scratch. Instead, they begin with prebuilt models, like using an API, which can be already performing tasks like summarizing, translating, or answering questions using generic knowledge. This set will help you in getting up and running quickly with a small level results. As your business matures, you can start to layer in your custom data. Think internal policies, frequently asked questions, or customer interactions. And then you can fine tune the model to behave the way your business needs it to behave. Now, your AI starts producing business-ready output, smarter, more relevant, and aligned with your tone, brand, or compliance needs. 13:45 Lois: Ok…let's think of AI deployment in the hybrid approach as following a pyramid or ladder like structure. Can you take us through the different levels? Yunus: So, on the top, quick start, minimal setup, great for business automation, which can be used as a pilot use case. So, if I'm taking off the shelf APIs or platforms, they can be giving me a faster, less set of requirements, and they are basically acting like a pilot use. Later, you can add your own data or logic so you can add your data. You can fine tune or change your business logic. And this is where fine tuning and prompt engineering helps tailor the AI to your workflows and your language. And then at the end, which is at the bottom, you build your own model. It is reserved for core capabilities or competitive advantages where total control and differentiation matters in building that particular model. You don't need to go all in from one day. So, start with what is available, like, use an off shelf, API, or platform, customize as you grow. Build only when it gives you a true edge. This is what we call the best of both worlds, build and buy. 15:05 Lois: Thank you so much, Yunus, for joining us again. To learn more about the topics covered today, visit mylearn.oracle.com and search for the AI for You course. Nikita: Join us next week for another episode of the Oracle University Podcast where we discuss the Oracle AI stack and Oracle AI services. Until then, this is Nikita Abraham… Lois: And Lois Houston, signing off! 15:29 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
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The AI Workflow
09/02/2025
The AI Workflow
Join Lois Houston and Nikita Abraham as they chat with Yunus Mohammed, a Principal Instructor at Oracle University, about the key stages of AI model development. From gathering and preparing data to selecting, training, and deploying models, learn how each phase impacts AI’s real-world effectiveness. The discussion also highlights why monitoring AI performance and addressing evolving challenges are critical for long-term success. AI for You: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. -------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hey everyone! In our last episode, we spoke about generative AI and gen AI agents. Today, we’re going to look at the key stages in a typical AI workflow. We’ll also discuss how data quality, feedback loops, and business goals influence AI success. With us today is Yunus Mohammed, a Principal Instructor at Oracle University. 01:00 Lois: Hi Yunus! We're excited to have you here! Can you walk us through the various steps in developing and deploying an AI model? Yunus: The first point is the collect data. We gather relevant data, either historical or real time. Like customer transactions, support tickets, survey feedbacks, or sensor logs. A travel company, for example, can collect past booking data to predict future demand. So, data is the most crucial and the important component for building your AI models. But it's not just the data. You need to prepare the data. In the prepared data process, we clean, organize, and label the data. AI can't learn from messy spreadsheets. We try to make the data more understandable and organized, like removing duplicates, filling missing values in the data with some default values or formatting dates. All these comes under organization of the data and give a label to the data, so that the data becomes more supervised. After preparing the data, I go for selecting the model to train. So now, we pick what type of model fits your goals. It can be a traditional ML model or a deep learning network model, or it can be a generative model. The model is chosen based on the business problems and the data we have. So, we train the model using the prepared data, so it can learn the patterns of the data. Then after the model is trained, I need to evaluate the model. You check how well the model performs. Is it accurate? Is it fair? The metrics of the evaluation will vary based on the goal that you're trying to reach. If your model misclassifies emails as spam and it is doing it very much often, then it is not ready. So I need to train it further. So I need to train it to a level when it identifies the official mail as official mail and spam mail as spam mail accurately. After evaluating and making sure your model is perfectly fitting, you go for the next step, which is called the deploy model. Once we are happy, we put it into the real world, like into a CRM, or a web application, or an API. So, I can configure that with an API, which is application programming interface, or I add it to a CRM, Customer Relationship Management, or I add it to a web application that I've got. Like for example, a chatbot becomes available on your company's website, and the chatbot might be using a generative AI model. Once I have deployed the model and it is working fine, I need to keep track of this model, how it is working, and need to monitor and improve whenever needed. So I go for a stage, which is called as monitor and improve. So AI isn't set in and forget it. So over time, there are lot of changes that is happening to the data. So we monitor performance and retrain when needed. An e-commerce recommendation model needs updates as there might be trends which are shifting. So the end user finally sees the results after all the processes. A better product, or a smarter service, or a faster decision-making model, if we do this right. That is, if we process the flow perfectly, they may not even realize AI is behind it to give them the accurate results. 04:59 Nikita: Got it. So, everything in AI begins with data. But what are the different types of data used in AI development? Yunus: We work with three main types of data: structured, unstructured, and semi-structured. Structured data is like a clean set of tables in Excel or databases, which consists of rows and columns with clear and consistent data information. Unstructured is messy data, like your email or customer calls that records videos or social media posts, so they all comes under unstructured data. Semi-structured data is things like logs on XML files or JSON files. Not quite neat but not entirely messy either. So they are, they are termed semi-structured. So structured, unstructured, and then you've got the semi-structured. 05:58 Nikita: Ok… and how do the data needs vary for different AI approaches? Yunus: Machine learning often needs labeled data. Like a bank might feed past transactions labeled as fraud or not fraud to train a fraud detection model. But machine learning also includes unsupervised learning, like clustering customer spending behavior. Here, no labels are needed. In deep learning, it needs a lot of data, usually unstructured, like thousands of loan documents, call recordings, or scan checks. These are fed into the models and the neural networks to detect and complex patterns. Data science focus on insights rather than the predictions. So a data scientist at the bank might use customer relationship management exports and customer demographies to analyze which age group prefers credit cards over the loans. Then we have got generative AI that thrives on diverse, unstructured internet scalable data. Like it is getting data from books, code, images, chat logs. So these models, like ChatGPT, are trained to generate responses or mimic the styles and synthesize content. So generative AI can power a banking virtual assistant trained on chat logs and frequently asked questions to answer customer queries 24/7. 07:35 Lois: What are the challenges when dealing with data? Yunus: Data isn't just about having enough. We must also think about quality. Is it accurate and relevant? Volume. Do we have enough for the model to learn from? And is my data consisting of any kind of unfairly defined structures, like rejecting more loan applications from a certain zip code, which actually gives you a bias of data? And also the privacy. Are we handling personal data responsibly or not? Especially data which is critical or which is regulated, like the banking sector or health data of the patients. Before building anything smart, we must start smart. 08:23 Lois: So, we’ve established that collecting the right data is non-negotiable for success. Then comes preparing it, right? Yunus: This is arguably the most important part of any AI or data science project. Clean data leads to reliable predictions. Imagine you have a column for age, and someone accidentally entered an age of like 999. That's likely a data entry error. Or maybe a few rows have missing ages. So we either fix, remove, or impute such issues. This step ensures our model isn't misled by incorrect values. Dates are often stored in different formats. For instance, a date, can be stored as the month and the day values, or it can be stored in some places as day first and month next. We want to bring everything into a consistent, usable format. This process is called as transformation. The machine learning models can get confused if one feature, like example the income ranges from 10,000 to 100,000, and another, like the number of kids, range from 0 to 5. So we normalize or scale values to bring them to a similar range, say 0 or 1. So we actually put it as yes or no options. So models don't understand words like small, medium, or large. We convert them into numbers using encoding. One simple way is assigning 1, 2, and 3 respectively. And then you have got removing stop words like the punctuations, et cetera, and break the sentence into smaller meaningful units called as tokens. This is actually used for generative AI tasks. In deep learning, especially for Gen AI, image or audio inputs must be of uniform size and format. 10:31 Lois: And does each AI system have a different way of preparing data? Yunus: For machine learning ML, focus is on cleaning, encoding, and scaling. Deep learning needs resizing and normalization for text and images. Data science, about reshaping, aggregating, and getting it ready for insights. The generative AI needs special preparation like chunking, tokenizing large documents, or compressing images. 11:06 Oracle University’s Race to Certification 2025 is your ticket to free training and certification in today’s hottest tech. Whether you’re starting with Artificial Intelligence, Oracle Cloud Infrastructure, Multicloud, or Oracle Data Platform, this challenge covers it all! Learn more about your chance to win prizes and see your name on the Leaderboard by visiting education.oracle.com/race-to-certification-2025. That’s education.oracle.com/race-to-certification-2025. 11:50 Nikita: Welcome back! Yunus, how does a user choose the right model to solve their business problem? Yunus: Just like a business uses different dashboards for marketing versus finance, in AI, we use different model types, depending on what we are trying to solve. Like classification is choosing a category. Real-world example can be whether the email is a spam or not. Use in fraud detection, medical diagnosis, et cetera. So what you do is you classify that particular data and then accurately access that classification of data. Regression, which is used for predicting a number, like, what will be the price of a house next month? Or it can be a useful in common forecasting sales demands or on the cost. Clustering, things without labels. So real-world examples can be segmenting customers based on behavior for targeted marketing. It helps discovering hidden patterns in large data sets. Generation, that is creating new content. So AI writing product description or generating images can be a real-world example for this. And it can be used in a concept of generative AI models like ChatGPT or Dall-E, which operates on the generative AI principles. 13:16 Nikita: And how do you train a model? Yunus: We feed it with data in small chunks or batches and then compare its guesses to the correct values, adjusting its thinking like weights to improve next time, and the cycle repeats until the model gets good at making predictions. So if you're building a fraud detection system, ML may be enough. If you want to analyze medical images, you will need deep learning. If you're building a chatbot, go for a generative model like the LLM. And for all of these use cases, you need to select and train the applicable models as and when appropriate. 14:04 Lois: OK, now that the model’s been trained, what else needs to happen before it can be deployed? Yunus: Evaluate the model, assess a model's accuracy, reliability, and real-world usefulness before it's put to work. That is, how often is the model right? Does it consistently perform well? Is it practical in the real world to use this model or not? Because if I have bad predictions, doesn't just look bad, it can lead to costly business mistakes. Think of recommending the wrong product to a customer or misidentifying a financial risk. So what we do here is we start with splitting the data into two parts. So we train the data by training data. And this is like teaching the model. And then we have got the testing data. This is actually used for checking how well the model has learned. So once trained, the model makes predictions. We compare the predictions to the actual answers, just like checking your answer after a quiz. We try to go in for tailored evaluation based on AI types. Like machine learning, we care about accuracy in prediction. Deep learning is about fitting complex data like voice or images, where the model repeatedly sees examples and tunes itself to reduce errors. Data science, we look for patterns and insights, such as which features will matter. In generative AI, we judge by output quality. Is it coherent, useful, and is it natural? The model improves with the accuracy and the number of epochs the training has been done on. 15:59 Nikita: So, after all that, we finally come to deploying the model… Yunus: Deploying a model means we are integrating it into our actual business system. So it can start making decisions, automating tasks, or supporting customer experiences in real time. Think of it like this. Training is teaching the model. Evaluating is testing it. And deployment is giving it a job. The model needs a home either in the cloud or inside your company's own servers. Think of it like putting the AI in place where it can be reached by other tools. Exposed via API or embedded in an app, or you can say application, this is how the AI becomes usable. Then, we have got the concept of receives live data and returns predictions. So receives live data and returns prediction is when the model listens to real-time inputs like a user typing, or user trying to search or click or making a transaction, and then instantly, your AI responds with a recommendation, decisions, or results. Deploying the model isn’t the end of the story. It is just the beginning of the AI's real-world journey. Models may work well on day one, but things change. Customer behavior might shift. New products get introduced in the market. Economic conditions might evolve, like the era of COVID, where the demand shifted and the economical conditions actually changed. 17:48 Lois: Then it’s about monitoring and improving the model to keep things reliable over time. Yunus: The monitor and improve loop is a continuous process that ensures an AI model remains accurate, fair, and effective after deployment. The live predictions, the model is running in real time, making decisions or recommendations. The monitor performance are those predictions still accurate and helpful. Is latency acceptable? This is where we track metrics, user feedbacks, and operational impact. Then, we go for detect issues, like accuracy is declining, are responses feeling biased, are customers dropping off due to long response times? And the next step will be to reframe or update the model. So we add fresh data, tweak the logic, or even use better architectures to deploy the uploaded model, and the new version replaces the old one and the cycle continues again. 18:58 Lois: And are there challenges during this step? Yunus: The common issues, which are related to monitor and improve consist of model drift, bias, and latency of failures. In model drift, the model becomes less accurate as the environment changes. Or bias, the model may favor or penalize certain groups unfairly. Latency or failures, if the model is too slow or fails unpredictably, it disrupts the user experience. Let's take the loan approvals. In loan approvals, if we notice an unusually high rejection rate due to model bias, we might retrain the model with more diverse or balanced data. For a chatbot, we watch for customer satisfaction, which might arise due to model failure and fine-tune the responses for the model. So in forecasting demand, if the predictions no longer match real trends, say post-pandemic, due to the model drift, we update the model with fresh data. 20:11 Nikita: Thanks for that, Yunus. Any final thoughts before we let you go? Yunus: No matter how advanced your model is, its effectiveness depends on the quality of the data you feed it. That means, the data needs to be clean, structured, and relevant. It should map itself to the problem you're solving. If the foundation is weak, the results will be also. So data preparation is not just a technical step, it is a business critical stage. Once deployed, AI systems must be monitored continuously, and you need to watch for drops in performance for any bias being generated or outdated logic, and improve the model with new data or refinements. That's what makes AI reliable, ethical, and sustainable in the long run. 21:09 Nikita: Yunus, thank you for this really insightful session. If you’re interested in learning more about the topics we discussed today, go to mylearn.oracle.com and search for the AI for You course. Lois: That’s right. You’ll find skill checks to help you assess your understanding of these concepts. In our next episode, we’ll discuss the idea of buy versus build in the context of AI. Until then, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 21:39 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
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Core AI Concepts – Part 3
08/26/2025
Core AI Concepts – Part 3
Join hosts Lois Houston and Nikita Abraham, along with Principal AI/ML Instructor Himanshu Raj, as they discuss the transformative world of Generative AI. Together, they uncover the ways in which generative AI agents are changing the way we interact with technology, automating tasks and delivering new possibilities. AI for You: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead of Editorial Services. Nikita: Hi everyone! Last week was Part 2 of our conversation on core AI concepts, where we went over the basics of data science. In Part 3 today, we’ll look at generative AI and gen AI agents in detail. To help us with that, we have Himanshu Raj, Principal AI/ML Instructor. Hi Himanshu, what’s the difference between traditional AI and generative AI? 01:01 Himanshu: So until now, when we talked about artificial intelligence, we usually meant models that could analyze information and make decisions based on it, like a judge who looks at evidence and gives a verdict. And that's what we call traditional AI that's focused on analysis, classification, and prediction. But with generative AI, something remarkable happens. Generative AI does not just evaluate. It creates. It's more like a storyteller who uses knowledge from the past to imagine and build something brand new. For example, instead of just detecting if an email is spam, generative AI could write an entirely new email for you. Another example, traditional AI might predict what a photo contains. Generative AI, on the other hand, creates a brand-new photo based on description. Generative AI refers to artificial intelligence models that can create entirely new content, such as text, images, music, code, or video that resembles human-made work. Instead of simple analyzing or predicting, generative AI produces something original that resembles what a human might create. 02:16 Lois: How did traditional AI progress to the generative AI we know today? Himanshu: First, we will look at small supervised learning. So in early days, AI models were trained on small labeled data sets. For example, we could train a model with a few thousand emails labeled spam or not spam. The model would learn simple decision boundaries. If email contains, "congratulations," it might be spam. This was efficient for a straightforward task, but it struggled with anything more complex. Then, comes the large supervised learning. As the internet exploded, massive data sets became available, so millions of images, billions of text snippets, and models got better because they had much more data and stronger compute power and thanks to advances, like GPUs, and cloud computing, for example, training a model on millions of product reviews to predict customer sentiment, positive or negative, or to classify thousands of images in cars, dogs, planes, etc. Models became more sophisticated, capturing deeper patterns rather than simple rules. And then, generative AI came into the picture, and we eventually reached a point where instead of just classifying or predicting, models could generate entirely new content. Generative AI models like ChatGPT or GitHub Copilot are trained on enormous data sets, not to simply answer a yes or no, but to create outputs that look and feel like human made. Instead of judging the spam or sentiment, now the model can write an article, compose a song, or paint a picture, or generate new software code. 03:55 Nikita: Himanshu, what motivated this sort of progression? Himanshu: Because of the three reasons. First one, data, we had way more of it thanks to the internet, smartphones, and social media. Second is compute. Graphics cards, GPUs, parallel computing, and cloud systems made it cheap and fast to train giant models. And third, and most important is ambition. Humans always wanted machines not just to judge existing data, but to create new knowledge, art, and ideas. 04:25 Lois: So, what’s happening behind the scenes? How is gen AI making these things happen? Himanshu: Generative AI is about creating entirely new things across different domains. On one side, we have large language models or LLMs. They are masters of generating text conversations, stories, emails, and even code. And on the other side, we have diffusion models. They are the creative artists of AI, turning text prompts into detailed images, paintings, or even videos. And these two together are like two different specialists. The LLM acts like a brain that understands and talks, and the diffusion model acts like an artist that paints based on the instructions. And when we connect these spaces together, we create something called multimodal AI, systems that can take in text and produce images, audio, or other media, opening a whole new range of possibilities. It can not only take the text, but also deal in different media options. So today when we say ChatGPT or Gemini, they can generate images, and it's not just one model doing everything. These are specialized systems working together behind the scenes. 05:38 Lois: You mentioned large language models and how they power text-based gen AI, so let’s talk more about them. Himanshu, what is an LLM and how does it work? Himanshu: So it's a probabilistic model of text, which means, it tries to predict what word is most likely to come next based on what came before. This ability to predict one word at a time intelligently is what builds full sentences, paragraphs, and even stories. 06:06 Nikita: But what’s large about this? Why’s it called a large language model? Himanshu: It simply means the model has lots and lots of parameters. And think of parameters as adjustable dials the model fine tuned during learning. There is no strict rule, but today, large models can have billions or even trillions of these parameters. And the more the parameters, more complex patterns, the model can understand and can generate a language better, more like human. 06:37 Nikita: Ok… and image-based generative AI is powered by diffusion models, right? How do they work? Himanshu: Diffusion models start with something that looks like pure random noise. Imagine static on an old TV screen. No meaningful image at all. From there, the model carefully removes noise step by step to create something more meaningful and think of it like sculpting a statue. You start with a rough block of stone and slowly, carefully you chisel away to reveal a beautiful sculpture hidden inside. And in each step of this process, the AI is making an educated guess based on everything it has learned from millions of real images. It's trying to predict. 07:24 Stay current by taking the 2025 Oracle Fusion Cloud Applications Delta Certifications. This is your chance to demonstrate your understanding of the latest features and prove your expertise by obtaining a globally recognized certification, all for free! Discover the certification paths, use the resources on MyLearn to prepare, and future-proof your skills. Get started now at mylearn.oracle.com. 07:53 Nikita: Welcome back! Himanshu, for most of us, our experience with generative AI is with text-based tools like ChatGPT. But I’m sure the uses go far beyond that, right? Can you walk us through some of them? Himanshu: First one is text generation. So we can talk about chatbots, which are now capable of handling nuanced customer queries in banking travel and retail, saving companies hours of support time. Think of a bank chatbot helping a customer understand mortgage options or virtual HR Assistant in a large company, handling leave request. You can have embedding models which powers smart search systems. Instead of searching by keywords, businesses can now search by meaning. For instance, a legal firm can search cases about contract violations in tech and get semantically relevant results, even if those exact words are not used in the documents. The third one, for example, code generation, tools like GitHub Copilot help developers write boilerplate or even functional code, accelerating software development, especially in routine or repetitive tasks. Imagine writing a waveform with just a few prompts. The second application, is image generation. So first obvious use is art. So designers and marketers can generate creative concepts instantly. Say, you need illustrations for a campaign on future cities. Generative AI can produce dozens of stylized visuals in minutes. For design, interior designers or architects use it to visualize room layouts or design ideas even before a blueprint is finalized. And realistic images, retail companies generate images of people wearing their clothing items without needing real models or photoshoots, and this reduces the cost and increase the personalization. Third application is multimodal systems, and these are combined systems that take one kind of input or a combination of different inputs and produce different kind of outputs, or can even combine various kinds, be it text image in both input and output. Text to image It's being used in e-commerce, movie concept art, and educational content creation. For text to video, this is still in early days, but imagine creating a product explainer video just by typing out the script. Marketing teams love this for quick turnarounds. And the last one is text to audio. Tools like ElevenLabs can convert text into realistic, human like voiceovers useful in training modules, audiobooks, and accessibility apps. So generative AI is no longer just a technical tool. It's becoming a creative copilot across departments, whether it's marketing, design, product support, and even operations. 10:42 Lois: That’s great! So, we’ve established that generative AI is pretty powerful. But what kind of risks does it pose for businesses and society in general? Himanshu: The first one is deepfakes. Generative AI can create fake but highly realistic media, video, audios or even faces that look and sound authentic. Imagine a fake video of a political leader announcing a policy, they never approved. This could cause mass confusion or even impact elections. In case of business, deepfakes can be also used in scams where a CEO's voice is faked to approve fraudulent transactions. Number two, bias, if AI is trained on biased historical data, it can reinforce stereotypes even when unintended. For example, a hiring AI system that favors male candidates over equally qualified women because of historical data was biased. And this bias can expose companies to discrimination, lawsuits, brand damage and ethical concerns. Number three is hallucinations. So sometimes AI system confidently generate information that is completely wrong without realizing it. Sometimes you ask a chatbot for a legal case summary, and it gives you a very convincing but entirely made up court ruling. In case of business impact, sectors like health care, finance, or law hallucinations can or could have serious or even dangerous consequences if not caught. The fourth one is copyright and IP issues, generative AI creates new content, but often, based on material it was trained on. Who owns a new work? A real life example could be where an artist finds their unique style was copied by an AI that was trained on their paintings without permission. In case of a business impact, companies using AI-generated content for marketing, branding or product designs must watch for legal gray areas around copyright and intellectual properties. So generative AI is not just a technology conversation, it's a responsibility conversation. Businesses must innovate and protect. Creativity and caution must go together. 12:50 Nikita: Let’s move on to generative AI agents. How is a generative AI agent different from just a chatbot or a basic AI tool? Himanshu: So think of it like a smart assistant, not just answering your questions, but also taking actions on your behalf. So you don't just ask, what's the best flight to Vegas? Instead, you tell the agent, book me a flight to Vegas and a room at the Hilton. And it goes ahead, understands that, finds the options, connects to the booking tools, and gets it done. So act on your behalf using goals, context, and tools, often with a degree of autonomy. Goals, are user defined outcomes. Example, I want to fly to Vegas and stay at Hilton. Context, this includes preferences history, constraints like economy class only or don't book for Mondays. Tools could be APIs, databases, or services it can call, such as a travel API or a company calendar. And together, they let the agent reason, plan, and act. 14:02 Nikita: How does a gen AI agent work under the hood? Himanshu: So usually, they go through four stages. First, one is understands and interprets your request like natural language understanding. Second, figure out what needs to be done, in this case flight booking plus hotel search. Third, retrieves data or connects to tools APIs if needed, such as Skyscanner, Expedia, or a Calendar. And fourth is takes action. That means confirming the booking and giving you a response like your travel is booked. Keep in mind not all gen AI agents are fully independent. 14:38 Lois: Himanshu, we’ve seen people use the terms generative AI agents and agentic AI interchangeably. What’s the difference between the two? Himanshu: Agentic AI is a broad umbrella. It refers to any AI system that can perceive, reason, plan, and act toward a goal and may improve and adapt over time. Most gen AI agents are reactive, not proactive. On the other hand, agentic AI can plan ahead, anticipate problems, and can even adjust strategies. So gen AI agents are often semi-autonomous. They act in predefined ways or with human approval. Agentic systems can range from low to full autonomy. For example, auto-GPT runs loops without user prompts and autonomous car decides routes and reactions. Most gen AI agents can only make multiple steps if explicitly designed that way, like a step-by-step logic flows in LangChain. And in case of agentic AI, it can plan across multiple steps with evolving decisions. On the memory and goal persistence, gen AI agents are typically stateless. That means they forget their goal unless you remind them. In case of agentic AI, these systems remember, adapt, and refine based on goal progression. For example, a warehouse robot optimizing delivery based on changing layouts. Some generative AI agents are agentic, like auto GPT. They use LLMs to reason, plan, and act, but not all. And likewise not all agentic AIs are generative. For example, an autonomous car, which may use computer vision control systems and planning, but no generative models. So agentic AI is a design philosophy or system behavior, which could be goal-driven, autonomous, and decision making. They can overlap, but as I said, not all generative AI agents are agentic, and not all agentic AI systems are generative. 16:39 Lois: What makes a generative AI agent actually work? Himanshu: A gen AI agent isn't just about answering the question. It's about breaking down a user's goal, figuring out how to achieve it, and then executing that plan intelligently. These agents are built from five core components and each playing a critical role. The first one is goal. So what is this agent trying to achieve? Think of this as the mission or intent. For example, if I tell the agent, help me organized a team meeting for Friday. So the goal in that case would be schedule a meeting. Number 2, memory. What does it remember? So this is the agent's context awareness. Storing previous chats, preferences, or ongoing tasks. For example, if last week I said I prefer meetings in the afternoon or I have already shared my team's availability, the agent can reuse that. And without the memory, the agent behaves stateless like a typical chatbot that forgets context after every prompt. Third is tools. What can it access? Agents aren't just smart, they are also connected. They can be given access to tools like calendars, CRMs, web APIs, spreadsheets, and so on. The fourth one is planner. So how does it break down the goal? And this is where the reasoning happens. The planner breaks big goals into a step-by-step plans, for example checking team availability, drafting meeting invite, and then sending the invite. And then probably, will confirm the booking. Agents don't just guess. They reason and organize actions into a logical path. And the fifth and final one is executor, who gets it done. And this is where the action takes place. The executor performs what the planner lays out. For example, calling APIs, sending message, booking reservations, and if planner is the architect, executor is the builder. 18:36 Nikita: And where are generative AI agents being used? Himanshu: Generative AI agents aren't just abstract ideas, they are being used across business functions to eliminate repetitive work, improve consistency, and enable faster decision making. For marketing, a generative AI agent can search websites and social platforms to summarize competitor activity. They can draft content for newsletters or campaign briefs in your brand tone, and they can auto-generate email variations based on audience segment or engagement history. For finance, a generative AI agent can auto-generate financial summaries and dashboards by pulling from ERP spreadsheets and BI tools. They can also draft variance analysis and budget reports tailored for different departments. They can scan regulations or policy documents to flag potential compliance risks or changes. For sales, a generative AI agent can auto-draft personalized sales pitches based on customer behavior or past conversations. They can also log CRM entries automatically once submitting summary is generated. They can also generate battlecards or next-step recommendations based on the deal stage. For human resource, a generative AI agent can pre-screen resumes based on job requirements. They can send interview invites and coordinate calendars. A common theme here is that generative AI agents help you scale your teams without scaling the headcount. 20:02 Nikita: Himanshu, let’s talk about the capabilities and benefits of generative AI agents. Himanshu: So generative AI agents are transforming how entire departments function. For example, in customer service, 24/7 AI agents handle first level queries, freeing humans for complex cases. They also enhance the decision making. Agents can quickly analyze reports, summarize lengthy documents, or spot trends across data sets. For example, a finance agent reviewing Excel data can highlight cash flow anomalies or forecast trends faster than a team of analysts. In case of personalization, the agents can deliver unique, tailored experiences without manual effort. For example, in marketing, agents generate personalized product emails based on each user's past behavior. For operational efficiency, they can reduce...
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Core AI Concepts – Part 2
08/19/2025
Core AI Concepts – Part 2
In this episode, Lois Houston and Nikita Abraham continue their discussion on AI fundamentals, diving into Data Science with Principal AI/ML Instructor Himanshu Raj. They explore key concepts like data collection, cleaning, and analysis, and talk about how quality data drives impactful insights. AI for You: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ---------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me today is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hi everyone! Last week, we began our exploration of core AI concepts, specifically machine learning and deep learning. I’d really encourage you to go back and listen to the episode if you missed it. 00:52 Lois: Yeah, today we’re continuing that discussion, focusing on data science, with our Principal AI/ML Instructor Himanshu Raj. Nikita: Hi Himanshu! Thanks for joining us again. So, let’s get cracking! What is data science? 01:06 Himanshu: It's about collecting, organizing, analyzing, and interpreting data to uncover valuable insights that help us make better business decisions. Think of data science as the engine that transforms raw information into strategic action. You can think of a data scientist as a detective. They gather clues, which is our data. Connect the dots between those clues and ultimately solve mysteries, meaning they find hidden patterns that can drive value. 01:33 Nikita: Ok, and how does this happen exactly? Himanshu: Just like a detective relies on both instincts and evidence, data science blends domain expertise and analytical techniques. First, we collect raw data. Then we prepare and clean it because messy data leads to messy conclusions. Next, we analyze to find meaningful patterns in that data. And finally, we turn those patterns into actionable insights that businesses can trust. 02:00 Lois: So what you’re saying is, data science is not just about technology; it's about turning information into intelligence that organizations can act on. Can you walk us through the typical steps a data scientist follows in a real-world project? Himanshu: So it all begins with business understanding. Identifying the real problem we are trying to solve. It's not about collecting data blindly. It's about asking the right business questions first. And once we know the problem, we move to data collection, which is gathering the relevant data from available sources, whether internal or external. Next one is data cleaning. Probably the least glamorous but one of the most important steps. And this is where we fix missing values, remove errors, and ensure that the data is usable. Then we perform data analysis or what we call exploratory data analysis. Here we look for patterns, prints, and initial signals hidden inside the data. After that comes the modeling and evaluation, where we apply machine learning or deep learning techniques to predict, classify, or forecast outcomes. Machine learning, deep learning are like specialized equipment in a data science detective's toolkit. Powerful but not the whole investigation. We also check how good the models are in terms of accuracy, relevance, and business usefulness. Finally, if the model meets expectations, we move to deployment and monitoring, putting the model into real world use and continuously watching how it performs over time. 03:34 Nikita: So, it’s a linear process? Himanshu: It's not linear. That's because in real world data science projects, the process does not stop after deployment. Once the model is live, business needs may evolve, new data may become available, or unexpected patterns may emerge. And that's why we come back to business understanding again, defining the questions, the strategy, and sometimes even the goals based on what we have learned. In a way, a good data science project behaves like living in a system which grows, adapts, and improves over time. Continuous improvement keeps it aligned with business value. Now, think of it like adjusting your GPS while driving. The route you plan initially might change as new traffic data comes in. Similarly, in data science, new information constantly help refine our course. The quality of our data determines the quality of our results. If the data we feed into our models is messy, inaccurate, or incomplete, the outputs, no matter how sophisticated the technology, will be also unreliable. And this concept is often called garbage in, garbage out. Bad input leads to bad output. Now, think of it like cooking. Even the world's best Michelin star chef can't create a masterpiece with spoiled or poor-quality ingredients. In the same way, even the most advanced AI models can't perform well if the data they are trained on is flawed. 05:05 Lois: Yeah, that's why high-quality data is not just nice to have, it’s absolutely essential. But Himanshu, what makes data good? Himanshu: Good data has a few essential qualities. The first one is complete. Make sure we aren't missing any critical field. For example, every customer record must have a phone number and an email. It should be accurate. The data should reflect reality. If a customer's address has changed, it must be updated, not outdated. Third, it should be consistent. Similar data must follow the same format. Imagine if the dates are written differently, like 2024/04/28 versus April 28, 2024. We must standardize them. Fourth one. Good data should be relevant. We collect only the data that actually helps solve our business question, not unnecessary noise. And last one, it should be timely. So data should be up to date. Using last year's purchase data for a real time recommendation engine wouldn't be helpful. 06:13 Nikita: Ok, so ideally, we should use good data. But that’s a bit difficult in reality, right? Because what comes to us is often pretty messy. So, how do we convert bad data into good data? I’m sure there are processes we use to do this. Himanshu: First one is cleaning. So this is about correcting simple mistakes, like fixing typos in city names or standardizing dates. The second one is imputation. So if some values are missing, we fill them intelligently, for instance, using the average income for a missing salary field. Third one is filtering. In this, we remove irrelevant or noisy records, like discarding fake email signups from marketing data. The fourth one is enriching. We can even enhance our data by adding trusted external sources, like appending credit scores from a verified bureau. And the last one is transformation. Here, we finally reshape data formats to be consistent, for example, converting all units to the same currency. So even messy data can become usable, but it takes deliberate effort, structured process, and attention to quality at every step. 07:26 Oracle University’s Race to Certification 2025 is your ticket to free training and certification in today’s hottest technology. Whether you’re starting with Artificial Intelligence, Oracle Cloud Infrastructure, Multicloud, or Oracle Data Platform, this challenge covers it all! Learn more about your chance to win prizes and see your name on the Leaderboard by visiting education.oracle.com/race-to-certification-2025. That’s education.oracle.com/race-to-certification-2025. 08:10 Nikita: Welcome back! Himanshu, we spoke about how to clean data. Now, once we get high-quality data, how do we analyze it? Himanshu: In data science, there are four primary types of analysis we typically apply depending on the business goal we are trying to achieve. The first one is descriptive analysis. It helps summarize and report what has happened. So often using averages, totals, or percentages. For example, retailers use descriptive analysis to understand things like what was the average customer spend last quarter? How did store foot traffic trend across months? The second one is diagnostic analysis. Diagnostic analysis digs deeper into why something happened. For example, hospitals use this type of analysis to find out, for example, why a certain department has higher patient readmission rates. Was it due to staffing, post-treatment care, or patient demographics? The third one is predictive analysis. Predictive analysis looks forward, trying to forecast future outcomes based on historical patterns. For example, energy companies predict future electricity demand, so they can better manage resources and avoid shortages. And the last one is prescriptive analysis. So it does not just predict. It recommends specific actions to take. So logistics and supply chain companies use prescriptive analytics to suggest the most efficient delivery routes or warehouse stocking strategies based on traffic patterns, order volume, and delivery deadlines. 09:42 Lois: So really, we’re using data science to solve everyday problems. Can you walk us through some practical examples of how it’s being applied? Himanshu: The first one is predictive maintenance. It is done in manufacturing a lot. A factory collects real time sensor data from machines. Data scientists first clean and organize this massive data stream, explore patterns of past failures, and design predictive models. The goal is not just to predict breakdowns but to optimize maintenance schedules, reducing downtime and saving millions. The second one is a recommendation system. It's prevalent in retail and entertainment industries. Companies like Netflix or Amazon gather massive user interaction data such as views, purchases, likes. Data scientists structure and analyze this behavioral data to find meaningful patterns of preferences and build models that suggest relevant content, eventually driving more engagement and loyalty. The third one is fraud detection. It's applied in finance and banking sector. Banks store vast amounts of transaction record records. Data scientists clean and prepare this data, understand typical spending behaviors, and then use statistical techniques and machine learning to spot unusual patterns, catching fraud faster than manual checks could ever achieve. The last one is customer segmentation, which is often applied in marketing. Businesses collect demographics and behavioral data about their customers. Instead of treating all the customers same, data scientists use clustering techniques to find natural groupings, and this insight helps businesses tailor their marketing efforts, offers, and communication for each of those individual groups, making them far more effective. Across all these examples, notice that data science isn't just building a model. Again, it's understanding the business need, reviewing the data, analyzing it thoughtfully, and building the right solution while helping the business act smarter. 11:44 Lois: Thank you, Himanshu, for joining us on this episode of the Oracle University Podcast. We can’t wait to have you back next week for part 3 of this conversation on core AI concepts, where we’ll talk about generative AI and gen AI agents. Nikita: And if you want to learn more about data science, visit mylearn.oracle.com and search for the AI for You course. Until next time, this is Nikita Abraham… Lois: And Lois Houston signing off! 12:13 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
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Core AI Concepts – Part 1
08/12/2025
Core AI Concepts – Part 1
Join hosts Lois Houston and Nikita Abraham, along with Principal AI/ML Instructor Himanshu Raj, as they dive deeper into the world of artificial intelligence, analyzing the types of machine learning. They also discuss deep learning, including how it works, its applications, and its advantages and challenges. From chatbot assistants to speech-to-text systems and image recognition, they explore how deep learning is powering the tools we use today. AI for You: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hi everyone! Last week, we went through the basics of artificial intelligence. If you missed it, I really recommend listening to that episode before you start this one. Today, we’re going to explore some foundational AI concepts, starting with machine learning. After that, we’ll discuss the two main machine learning models: supervised learning and unsupervised learning. And we’ll close with deep learning. Lois: Himanshu Raj, our Principal AI/ML Instructor, joins us for today’s episode. Hi Himanshu! Let’s dive right in. What is machine learning? 01:12 Himanshu: Machine learning lets computers learn from examples to make decisions or predictions without being told exactly what to do. They help computers learn from past data and examples so they can spot patterns and make smart decisions just like humans do, but faster and at scale. 01:31 Nikita: Can you give us a simple analogy so we can understand this better? Himanshu: When you train a dog to sit or fetch, you don't explain the logic behind the command. Instead, you give this dog examples and reinforce correct behavior with rewards, which could be a treat, a pat, or a praise. Over time, the dog learns to associate the command with the action and reward. Machine learning learns in a similar way, but with data instead of dog treats. We feed a mathematical system called models with multiple examples of input and the desired output, and it learns the pattern. It's trial and error, learning from the experience. Here is another example. Recognizing faces. Humans are incredibly good at this, even as babies. We don't need someone to explain every detail of the face. We just see many faces over time and learn the patterns. Machine learning models can be trained the same way. We showed them thousands or millions of face images, each labeled, and they start to detect patterns like eyes, nose, mouth, spacing, different angles. So eventually, they can recognize faces they have seen before or even match new ones that are similar. So machine learning doesn't have any rules, it's just learning from examples. This is the kind of learning behind things like face ID on your smartphone, security systems that recognizes employees, or even Facebook tagging people in your photos. 03:05 Lois: So, what you’re saying is, in machine learning, instead of telling the computer exactly what to do in every situation, you feed the model with data and give it examples of inputs and the correct outputs. Over time, the model figures out patterns and relationships within the data on its own, and it can make the smart guess when it sees something new. I got it! Now let’s move on to how machine learning actually works? Can you take us through the process step by step? Himanshu: Machine learning actually happens in three steps. First, we have the input, which is the training data. Think of this as showing the model a series of examples. It could be images of historical sales data or customer complaints, whatever we want the machine to learn from. Next comes the pattern finding. This is the brain of the system where the model starts spotting relationships in the data. It figures out things like customer who churn or leave usually contacts support twice in the same month. It's not given rules, it just learns patterns based on the example. And finally, we have output, which is the prediction or decision. This is the result of all this learning. Once trained, the computer or model can say this customer is likely to churn or leave. It's like having a smart assistant that makes fast, data-driven guesses without needing step by step instruction. 04:36 Nikita: What are the main elements in machine learning? Himanshu: In machine learning, we work with two main elements, features and labels. You can think of features as the clues we provide to the model, pieces of information like age, income, or product type. And the label is the solution we want the model to predict, like whether a customer will buy or not. 04:55 Nikita: Ok, I think we need an example here. Let’s go with the one you mentioned earlier about customers who churn. Himanshu: Imagine we have a table with data like customer age, number of visits, whether they churned or not. And each of these rows is one example. The features are age and visit count. The label is whether the customer churned, that is yes or no. Over the time, the model might learn patterns like customer under 30 who visit only once are more likely to leave. Or frequent visitors above age 45 rarely churn. If features are the clues, then the label is the solution, and the model is the brain of the system. It's what's the machine learning builds after learning from many examples, just like we do. And again, the better the features are, the better the learning. ML is just looking for patterns in the data we give it. 05:51 Lois: Ok, we’re with you so far. Let’s talk about the different types of machine learning. What is supervised learning? Himanshu: Supervised learning is a type of machine learning where the model learns from the input data and the correct answers. Once trained, the model can use what it learned to predict the correct answer for new, unseen inputs. Think of it like a student learning from a teacher. The teacher shows labeled examples like an apple and says, "this is an apple." The student receives feedback whether their guess was right or wrong. Over time, the student learns to recognize new apples on their own. And that's exactly how supervised learning works. It's learning from feedback using labeled data and then make predictions. 06:38 Nikita: Ok, so supervised learning means we train the model using labeled data. We already know the right answers, and we're essentially teaching the model to connect the dots between the inputs and the expected outputs. Now, can you give us a few real-world examples of supervised learning? Himanshu: First, house price prediction. In this case, we give the model features like a square footage, location, and number of bedrooms, and the label is the actual house price. Over time, it learns how to predict prices for new homes. The second one is email: spam or not. In this case, features might include words in the subject line, sender, or links in the email. The label is whether the email is spam or not. The model learns patterns to help us filter our inbox, as you would have seen in your Gmail inboxes. The third one is cat versus dog classification. Here, the features are the pixels in an image, and the label tells us whether it's a cat or a dog. After seeing many examples, the model learns to tell the difference on its own. Let's now focus on one very common form of supervised learning, that is regression. Regression is used when we want to predict a numerical value, not a category. In simple terms, it helps answer questions like, how much will it be? Or what will be the value be? For example, predicting the price of a house based on its size, location, and number of rooms. Or estimating next quarter's revenue based on marketing spend. 08:18 Lois: Are there any other types of supervised learning? Himanshu: While regression is about predicting a number, classification is about predicting a category or type. You can think of it as the model answering is this yes or no, or which group does this belong to. Classification is used when the goal is to predict a category or a class. Here, the model learns patterns from historical data where both the input variables, known as features, and the correct categories, called labels, are already known. 08:53 Ready to level-up your cloud skills? The 2025 Oracle Fusion Cloud Applications Certifications are here! These industry-recognized credentials validate your expertise in the latest Oracle Fusion Cloud solutions, giving you a competitive edge and helping drive real project success and customer satisfaction. Explore the certification paths, prepare with MyLearn, and position yourself for the future. Visit mylearn.oracle.com to get started today. 09:25 Nikita: Welcome back! So that was supervised machine learning. What about unsupervised machine learning, Himanshu? Himanshu: Unlike supervised learning, here, the model is not given any labels or correct answers. It just handed the raw input data and left to make sense of it on its own. The model explores the data and discovers hidden patterns, groupings, or structures on its own, without being explicitly told what to look for. And it's more like a student learning from observations and making their own inferences. 09:55 Lois: Where is unsupervised machine learning used? Can you take us through some of the use cases? Himanshu: The first one is product recommendation. Customers are grouped based on shared behavior even without knowing their intent. This helps show what the other users like you also prefer. Second one is anomaly detection. Unusual patterns, such as fraud, network breaches, or manufacturing defects, can stand out, all without needing thousands of labeled examples. And third one is customer segmentation. Customers can be grouped by purchase history or behavior to tailor experiences, pricing, or marketing campaigns. 10:32 Lois: And finally, we come to deep learning. What is deep learning, Himanshu? Himanshu: Humans learn from experience by seeing patterns repeatedly. Brain learns to recognize an image by seeing it many times. The human brain contains billions of neurons. Each neuron is connected to others through synapses. Neurons communicate by passing signals. The brain adjusts connections based on repeated stimuli. Deep learning was inspired by how the brain works using artificial neurons and connections. Just like our brains need a lot of examples to learn, so do the deep learning models. The more the layers and connections are, the more complex patterns it can learn. The brain is not hard-coded. It learns from patterns. Deep learning follows the same idea. Metaphorically speaking, a deep learning model can have over a billion neurons, more than a cat's brain, which have around 250 million neurons. Here, the neurons are mathematical units, often called nodes, or simply as units. Layers of these units are connected, mimicking how biological neurons interact. So deep learning is a type of machine learning where the computer learns to understand complex patterns. What makes it special is that it uses neural networks with many layers, which is why we call it deep learning. 11:56 Lois: And how does deep learning work? Himanshu: Deep learning is all about finding high-level meaning from low-level data layer by layer, much like how our brains process what we see and hear. A neural network is a system of connected artificial neurons, or nodes, that work together to learn patterns and make decisions. 12:15 Nikita: I know there are different types of neural networks, with ANNs or Artificial Neural Networks being the one for general learning. How is it structured? Himanshu: There is an input layer, which is the raw data, which could be an image, sentence, numbers, a hidden layer where the patterns are detected or the features are learned, and the output layer where the final decision is made. For example, given an image, is this a dog? A neural network is like a team of virtual decision makers, called artificial neurons, or nodes, working together, which takes input data, like a photo, and passes it through layers of neurons. And each neuron makes a small judgment and passes its result to the next layer. This process happens across multiple layers, learning more and more complex patterns as it goes, and the final layer gives the output. Imagine a factory assembly line where each station, or the layer, refines the input a bit more. By the end, you have turned raw parts into something meaningful. And this is a very simple analogy. This structure forms the foundations of many deep learning models. More advanced architectures, like convolutional neural networks, CNNs, for images, or recurrent neural networks, RNN, for sequences built upon this basic idea. So, what I meant is that the ANN is the base structure, like LEGO bricks. CNNs and RNNs use those same bricks, but arrange them in a way that are better suited for images, videos, or sequences like text or speech. 13:52 Nikita: So, why do we call it deep learning? Himanshu: The word deep in deep learning does not refer to how profound or intelligent the model is. It actually refers to the number of layers in the neural network. It starts with an input layer, followed by hidden layers, and ends with an output layer. The layers are called hidden, in the sense that these are black boxes and their data is not visible or directly interpretable to the user. Models which has only one hidden layer is called shallow learning. As data moves, each layer builds on what the previous layer has learned. So layer one might detect a very basic feature, like edges or colors in an image. Layer two can take those edges and starts forming shapes, like curves or lines. And layer three use those shapes to identify complete objects, like a face, a car, or a person. This hierarchical learning is what makes deep learning so powerful. It allows the model to learn abstract patterns and generalize across complex data, whether it's visual, audio, or even language. And that's the essence of deep learning. It's not just about layers. It's about how each layer refines the information and one step closer to understanding. 15:12 Nikita: Himanshu, where does deep learning show up in our everyday lives? Himanshu: Deep learning is not just about futuristic robots, it's already powering the tools we use today. So think of when you interact with a virtual assistant on a website. Whether you are booking a hotel, resolving a banking issue, or asking customer support questions, behind the scenes, deep learning models understand your text, interpret your intent, and respond intelligently. There are many real-life examples, for example, ChatGPT, Google's Gemini, any airline website’s chatbots, bank's virtual agent. The next one is speech-to-text systems. Example, if you have ever used voice typing on your phone, dictated a message to Siri, or used Zoom's live captions, you have seen this in action already. The system listens to your voice and instantly converts it into a text. And this saves time, enhances accessibility, and helps automate tasks, like meeting transcriptions. Again, you would have seen real-life examples, such as Siri, Google Assistant, autocaptioning on Zoom, or YouTube Live subtitles. And lastly, image recognition. For example, hospitals today use AI to detect early signs of cancer in x-rays and CT scans that might be missed by the human eye. Deep learning models can analyze visual patterns, like a suspicious spot on a lung's X-ray, and flag abnormalities faster and more consistently than humans. Self-driving cars recognize stop signs, pedestrians, and other vehicles using the same technology. So, for example, cancer detection in medical imaging, Tesla's self-driving navigation, security system synchronizes face are very prominent examples of image recognition. 17:01 Lois: Deep learning is one of the most powerful tools we have today to solve complex problems. But like any tool, I’m sure it has its own set of pros and cons. What are its advantages, Himanshu? Himanshu: It is high accuracy. When trained with enough data, deep learning models can outperform humans. For example, again, spotting early signs of cancer in X-rays with higher accuracy. Second is handling of unstructured data. Deep learning shines when working with messy real-world data, like images, text, and voice. And it's why your phone can recognize your face or transcribe your speech into text. The third one is automatic pattern learning. Unlike traditional models that need hand-coded features, deep learning models figure out important patterns by themselves, making them extremely flexible. And the fourth one is scalability. Once trained, deep learning systems can scale easily, serving millions of customers, like Netflix recommending movies personalized to each one of us. 18:03 Lois: And what about its challenges? Himanshu: The first one is data and resource intensive. So deep learning demands huge amount of labeled data and powerful computing hardware, which means high cost, especially during training. The second thing is lacks explainability. These models often act like a black box. We know the output, but it's hard to explain exactly how the model reached that decision. This becomes a problem in areas like health care and finance where transparency is critical. The third challenge is vulnerability to bias. If the data contains biases, like favoring certain groups, the model will learn and amplify those biases unless we manage them carefully. The fourth and last challenge is it's harder to debug and maintain. Unlike a traditional software program, it's tough to manually correct a deep learning model if it starts behaving unpredictably. It requires retraining with new data. So deep learning offers powerful opportunities to solve complex problems using data, but it also brings challenges that require careful strategy, resources, and responsible use. 19:13 Nikita: We’re taking away a lot from this conversation. Thank you so much for your insights, Himanshu. Lois: If you’re interested to learn more, make sure you log into mylearn.oracle.com and look for the AI for You course. Join us next week for part 2 of the discussion on AI Concepts & Terminology, where we’ll focus on Data Science. Until then, this is Lois Houston… Nikita: And Nikita Abraham signing off! 19:39 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
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What is AI?
08/05/2025
What is AI?
In this episode, hosts Lois Houston and Nikita Abraham, together with Senior Cloud Engineer Nick Commisso, break down the basics of artificial intelligence (AI). They discuss the differences between Artificial General Intelligence (AGI) and Artificial Narrow Intelligence (ANI), and explore the concepts of machine learning, deep learning, and generative AI. Nick also shares examples of how AI is used in everyday life, from navigation apps to spam filters, and explains how AI can help businesses cut costs and boost revenue. AI for You: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ----------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Nikita: Hello and welcome to the Oracle University Podcast. I’m Nikita Abraham, Team Lead of Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi everyone! Welcome to a new season of the podcast. I’m so excited about this one because we’re going to dive into the world of artificial intelligence, speaking to many experts in the field. Nikita: If you've been listening to us for a while, you probably know we’ve covered AI from a bunch of different angles. But this time, we’re dialing it all the way back to basics. We wanted to create something for the absolute beginner, so no jargon, no assumptions, just simple conversations that anyone can follow. 01:08 Lois: That’s right, Niki. You don’t need to have a technical background or prior experience with AI to get the most out of these episodes. In our upcoming conversations, we’ll break down the basics of AI, explore how it's shaping the world around us, and understand its impact on your business. Nikita: The idea is to give you a practical understanding of AI that you can use in your work, especially if you’re in sales, marketing, operations, HR, or even customer service. 01:37 Lois: Today, we’ll talk about the basics of AI with Senior Cloud Engineer Nick Commisso. Hi Nick! Welcome back to the podcast. Can you tell us about human intelligence and how it relates to artificial intelligence? And within AI, I know we have Artificial General Intelligence, or AGI, and Artificial Narrow Intelligence, or ANI. What’s the difference between the two? Nick: Human intelligence is the intellectual capability of humans that allow us to learn new skills through observation and mental digestion, to think through and understand abstract concepts and apply reasoning, to communicate using language and understand non-verbal cues, such as facial expressions, tone variation, body language. We can handle objections and situations in real time, even in a complex setting. We can plan for short and long-term situations or projects. And we can create music, art, or invent something new or have original ideas. If machines can replicate a wide range of human cognitive abilities, such as learning, reasoning, or problem solving, we call it artificial general intelligence. Now, AGI is hypothetical for now, but when we apply AI to solve problems with specific, narrow objectives, we call it artificial narrow intelligence, or ANI. AGI is a hypothetical AI that thinks like a human. It represents the ultimate goal of artificial intelligence, which is a system capable of chatting, learning, and even arguing like us. If AGI existed, it would take the form like a robot doctor that accurately diagnoses and comforts patients, or an AI teacher that customizes lessons in real time based on each student's mood, pace, and learning style, or an AI therapist that comprehends complex emotions and provides empathetic, personalized support. ANI, on the other hand, focuses on doing one thing really well. It's designed to perform specific tasks by recognizing patterns and following rules, but it doesn't truly understand or think beyond its narrow scope. Think of ANI as a specialist. Your phone's face ID can recognize you instantly, but it can't carry on a conversation. Google Maps finds the best route, but it can't write you a poem. And spam filters catch junk mail, but it can't make you coffee. So, most of the AI you interact with today is ANI. It's smart, efficient, and practical, but limited to specific functions without general reasoning or creativity. 04:22 Nikita: Ok then what about Generative AI? Nick: Generative AI is a type of AI that can produce content such as audio, text, code, video, and images. ChatGPT can write essays, but it can't fact check itself. DALL-E creates art, but it doesn't actually know if it's good. Or AI song covers can create deepfakes like Drake singing "Baby Shark." 04:47 Lois: Why should I care about AI? Why is it important? Nick: AI is already part of your everyday life, often working quietly in the background. ANI powers things like navigation apps, voice assistants, and spam filters. Generative AI helps create everything from custom playlists to smart writing tools. And while AGI isn't here yet, it's shaping ideas about what the future might look like. Now, AI is not just a buzzword, it's a tool that's changing how we live, work, and interact with the world. So, whether you're using it or learning about it or just curious, it's worth knowing what's behind the tech that's becoming part of everyday life. 05:32 Lois: Nick, whenever people talk about AI, they also throw around terms like machine learning and deep learning. What are they and how do they relate to AI? Nick: As we shared earlier, AI is the ability of machines to imitate human intelligence. And Machine Learning, or ML, is a subset of AI where the algorithms are used to learn from past data and predict outcomes on new data or to identify trends from the past. Deep Learning, or DL, is a subset of machine learning that uses neural networks to learn patterns from complex data and make predictions or classifications. And Generative AI, or GenAI, on the other hand, is a specific application of DL focused on creating new content, such as text, images, and audio, by learning the underlying structure of the training data. 06:24 Nikita: AI is often associated with key domains like language, speech, and vision, right? So, could you walk us through some of the specific tasks or applications within each of these areas? Nick: Language-related AI tasks can be text related or generative AI. Text-related AI tasks use text as input, and the output can vary depending on the task. Some examples include detecting language, extracting entities in a text, extracting key phrases, and so on. 06:54 Lois: Ok, I get you. That’s like translating text, where you can use a text translation tool, type your text in the box, choose your source and target language, and then click Translate. That would be an example of a text-related AI task. What about generative AI language tasks? Nick: These are generative, which means the output text is generated by the model. Some examples are creating text, like stories or poems, summarizing texts, and answering questions, and so on. 07:25 Nikita: What about speech and vision? Nick: Speech-related AI tasks can be audio related or generative AI. Speech-related AI tasks use audio or speech as input, and the output can vary depending on the task. For example, speech to text conversion, speaker recognition, or voice conversion, and so on. Generative AI tasks are generative, i.e., the output audio is generated by the model (for example, music composition or speech synthesis). Vision-related AI tasks can be image related or generative AI. Image-related AI tasks use an image as the input, and the output depends on the task. Some examples are classifying images or identifying objects in an image. Facial recognition is one of the most popular image-related tasks that's often used for surveillance and tracking people in real time. It's used in a lot of different fields, like security and biometrics, law enforcement, entertainment, and social media. For generative AI tasks, the output image is generated by the model. For example, creating an image from a textual description or generating images of specific style or high resolution, and so on. It can create extremely realistic new images and videos by generating original 3D models of objects, such as machine, buildings, medications, people and landscapes, and so much more. 08:58 Lois: This is so fascinating. So, now we know what AI is capable of. But Nick, what is AI good at? Nick: AI frees you to focus on creativity and more challenging parts of your work. Now, AI isn't magic. It's just very good at certain tasks. It handles work that's repetitive, time consuming, or too complex for humans, like processing data or spotting patterns in large data sets. AI can take over routine tasks that are essential but monotonous. Examples include entering data into spreadsheets, processing invoices, or even scheduling meetings, freeing up time for more meaningful work. AI can support professionals by extending their abilities. Now, this includes tools like AI-assisted coding for developers, real-time language translation for travelers or global teams, and advanced image analysis to help doctors interpret medical scans much more accurately. 10:00 Nikita: And what would you say is AI's sweet spot? Nick: That would be tasks that are both doable and valuable. A few examples of tasks that are feasible technically and have business value are things like predicting equipment failure. This saves downtime and the loss of business. Call center automation, like the routing of calls to the right person. This saves time and improves customer satisfaction. Document summarization and review. This helps save time for busy professionals. Or inspecting power lines. Now, this task is dangerous. By automating it, it protects human life and saves time. 10:48 Oracle University’s Race to Certification 2025 is your ticket to free training and certification in today’s hottest tech. Whether you’re starting with Artificial Intelligence, Oracle Cloud Infrastructure, Multicloud, or Oracle Data Platform, this challenge covers it all! Learn more about your chance to win prizes and see your name on the Leaderboard by visiting education.oracle.com/race-to-certification-2025. That’s education.oracle.com/race-to-certification-2025. 11:30 Nikita: Welcome back! Now one big way AI is helping businesses today is by cutting costs, right? Can you give us some examples of this? Nick: Now, AI can contribute to cost reduction in several key areas. For instance, chatbots are capable of managing up to 50% of customer queries. This significantly reduces the need for manual support, thereby lowering operational costs. AI can streamline workflows, for example, reducing invoice processing time from 10 days to just 1 hour. This leads to substantial savings in both time and resources. In addition to cost savings, AI can also support revenue growth. One way is enabling personalization and upselling. Platforms like Netflix use AI-driven recommendation systems to influence user choices. This not only enhances the user experience, but it also increases the engagement and the subscription revenue. Or unlocking new revenue streams. AI technologies, such as generative video tools and virtual influencers, are creating entirely new avenues for advertising and branded content, expanding business opportunities in emerging markets. 12:50 Lois: Wow, saving money and boosting bottom lines. That’s a real win! But Nick, how is AI able to do this? Nick: Now, data is what teaches AI. Just like we learn from experience, so does AI. It learns from good examples, bad examples, and sometimes even the absence of examples. The quality and variety of data shape how smart, accurate, and useful AI becomes. Imagine teaching a kid to recognize animals using only pictures of squirrels that are labeled dogs. That would be very confusing at the dog park. AI works the exact same way, where bad data leads to bad decisions. With the right data, AI can be powerful and accurate. But with poor or biased data, it can become unreliable and even misleading. AI amplifies whatever you feed it. So, give it gourmet data, not data junk food. AI is like a chef. It needs the right ingredients. It needs numbers for predictions, like will this product sell? It needs images for cool tricks like detecting tumors, and text for chatting, or generating excuses for why you'd be late. Variety keeps AI from being a one-trick pony. Examples of the types of data are numbers, or machine learning, for predicting things like the weather. Text or generative AI, where chatbots are used for writing emails or bad poetry. Images, or deep learning, can be used for identifying defective parts in an assembly line, or an audio data type to transcribe a dictation from a doctor to a text. 14:35 Lois: With so much data available, things can get pretty confusing, which is why we have the concept of labeled and unlabeled data. Can you help us understand what that is? Nick: Labeled data are like flashcards, where everything has an answer. Spam filters learned from emails that are already marked as junk, and X-rays are marked either normal or pneumonia. Let's say we're training AI to tell cats from dogs, and we show it a hundred labeled pictures. Cat, dog, cat, dog, etc. Over time, it learns, hmm fluffy and pointy ears? That's probably a cat. And then we test it with new pictures to verify. Unlabeled data is like a mystery box, where AI has to figure it out itself. Social media posts, or product reviews, have no labels. So, AI clusters them by similarity. AI finding trends in unlabeled data is like a kid sorting through LEGOs without instructions. No one tells them which blocks will go together. 15:36 Nikita: With all the data that’s being used to train AI, I’m sure there are issues that can crop up too. What are some common problems, Nick? Nick: AI's performance depends heavily on the quality of its data. Poor or biased data leads to unreliable and unfair outcomes. Dirty data includes errors like typos, missing values, or duplicates. For example, an age record as 250, or NA, can confuse the AI. And a variety of data cleaning techniques are available, like missing data can be filled in, or duplicates can be removed. AI can inherit human prejudices if the data is unbalanced. For example, a hiring AI may favor one gender if the past three hires were mostly male. Ensuring diverse and representative data helps promote fairness. Good data is required to train better AI. Data could be messy, and needs to be processed before to train AI. 16:39 Nikita: Thank you, Nick, for sharing your expertise with us. To learn more about AI, go to mylearn.oracle.com and search for the AI for You course. As you complete the course, you’ll find skill checks that you can attempt to solidify your learning. Lois: In our next episode, we’ll dive deep into fundamental AI concepts and terminologies. Until then, this is Lois Houston… Nikita: And Nikita Abraham signing off! 17:05 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
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Modernize Your Business with Oracle Cloud Apps – Part 2
07/29/2025
Modernize Your Business with Oracle Cloud Apps – Part 2
In this episode, hosts Lois Houston and Nikita Abraham welcome back Cloud Delivery Lead Sarah Mahalik for a detailed tour of the four pillars of Oracle Fusion Cloud Applications: ERP, HCM, SCM, and CX. Discover how Oracle weaves AI, analytics, and automation into every layer of enterprise operations. Plus, learn how Oracle Modern Best Practice is redefining digital workflows. Oracle Fusion Cloud Applications: Process Essentials Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and joining me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hi everyone! Last week, we spoke about Oracle Cloud Apps and the Redwood design system. Today, we’ll take a closer look at the four key pillars of Oracle Cloud Apps. Lois: And we’re so excited to have Sarah Mahalik back with us. Sarah is a Cloud Delivery Lead here at Oracle. Hi Sarah! In the last episode, we briefly spoke about the various Oracle Cloud Apps offerings and their capabilities. For anyone who missed that episode, can you give us a quick introduction? 01:06 Sarah: Oracle Cloud Applications is an incredibly broad suite that covers many of the most important business functions, from Human Capital Management, Supply Chain Management, to Enterprise Resource Planning and Customer Experience. The products in the Oracle Fusion Cloud Applications suite are organized by functional groups or pillars. All of these applications sit on Oracle Cloud Infrastructure, a foundation built from scratch to support mission-critical applications. Oracle Fusion Applications deliver a single source of truth, enabling quick responses to disruptions and market opportunities. With unified data and consistent business rules, teams can build streamlined end-to-end processes, access real time analytics, and make faster data-driven decisions for improved outcomes. 01:52 Nikita: Ok, let’s actually get into each of these areas. I think we can start with Human Capital Management. Sarah: Oracle Human Capital Management is an end-to-end solution that allows you to manage all aspects of people data from hire to retire. It all starts with recruiting, or requisitions are used to advertise vacant positions, and candidates are managed through the hiring process. After recruitment, successful candidates are transferred to the human resources module. You can configure the organization structure to mirror that of your business. And this allows for easy reorganization whenever the structure changes. People data is a staple element of HCM. Therefore, as part of this product, an HR specialist can manage everything about the employee life cycle, including promotions, transfers, general assignment changes, and terminations. A robust self-service offering allows employees and managers to take ownership and responsibility for the data pertaining to themselves and their teams. By removing the burden of simple data processing from the HR specialists, it not only eases the pressure on the HR department but allows them to concentrate on more specialized tasks. 03:00 Lois: And how are the core products of HCM categorized? Sarah: The core products of Human Capital Management are categorized into four main groupings according to their logical purpose. First up, we have our human resources. This grouping includes the elements for implementing and maintaining the enterprise and workforce structure and employee life cycle data. This is where you would configure the organization structure as well as manage an employee's data from the HR specialist point of view. In addition, modules such as benefits, work life, workforce modeling and planning, and advanced HCM controls also sit within this category. This brings us to talent management. This category is one of the largest because it includes recruiting, learning, goals and performance management, career development, succession planning, talent reviews, and compensation. In addition to that, dynamic skills and opportunity marketplace are also included in this grouping. Within workforce management, you'll find absence management and time and labor. These naturally sit together because most organizations that implement both configure it so that an employee can enter both work time and absences on a time card, instead of having to visit two different entry points. You'll also find workforce health and safety here. And finally, payroll. All aspects of payroll are included here, whether you're simply using global payroll or localizations, such as UK, Canada, and Mexico. It also encompasses payroll interface for those organizations that run their payroll from another system, and just need to extract and migrate the relevant data from Fusion HCM Cloud. When talking about HCM systems, we cannot forget the employee self-service aspect of the product. For this, there's an employee experience module called Oracle Me. Here you'll find options, such as HCM communicate, touchpoints, journeys, HR help desk, and Oracle digital assistant. All of these combined enable an employee to take control and ownership of their own data, and use the many self-help options to get the information they need quickly and efficiently. In order to control how the system behaves and how users interact with it and perform the various processes, there are configuration options. These options allow organizations to define such things as the user experience, workflows, and approval policies based on their business requirements. And to meet the constant need for reporting, there's analytics, planning, and data modeling. And in addition to all of that, you can use configuration options, such as extensibility, integration, or import and extracts, security, and adaptive intelligence to help enhance the system and have it working and looking the way you need. Of course, much of these latter configuration items are not exclusive to HCM but are available for the Oracle Fusion Cloud as a whole. 05:47 Lois: That’s great. Ok, let’s move on to Oracle Enterprise Resource Planning, or ERP. Sarah: This is a complete modern Cloud ERP suite that provides your teams with advanced capabilities, such as AI, to automate the manual processes that slow them down, analytics to react to market shifts in real time, and automatic updates to stay current and gain a competitive advantage. Oracle Cloud ERP automates the entire Record to Report process and provides a common repository of information for global financial reporting and compliance. Within ERP, we have the broadest and deepest suite offering everything you need, from financials, project management, enterprise performance management, risk management and compliance, and analytics. 06:34 Nikita: Sarah, could you break down the different modules within ERP? Sarah: First, we have Financials, which is a global financial platform that connects and automates your financial management processes, including payables, receivables, fixed assets, expenses, and reporting for a clear view into your total financial health. Oracle Project Management offers a single project cloud solution designed to help you gain a complete picture of your organization's project finances and operations. It's seamlessly integrated across the enterprise with the Oracle Fusion Cloud ERP, HCM, and SCM applications. Oracle Fusion Cloud Enterprise Performance Management, or EPM, helps you model and plan across finance, HR, supply chain and sales, streamline the financial close process, and drive better decisions. Oracle Fusion Cloud Risk Management and Compliance is a security and audit solution that controls user access to your Oracle Cloud ERP financial data, monitors user activity, and makes it easier to meet compliance regulations through automation. Oracle Risk Management Compliance uses AI and ML to strengthen financial controls to help prevent cash leaks, enforce audit, and protect against emerging risks, saving you hours of manual work. Oracle Analytics for Cloud ERP complements the embedded analytics in Cloud ERP to provide pre-packaged use cases, predictive analysis, and KPIs based on variance analysis and historical trends. 08:06 Lois: And what about Supply Chain Management? Sarah: Oracle Supply Chain Management empowers organizations to plan, source, make, deliver, and service goods with agility and resilience. It offers a solution that integrates advanced capabilities, such as AI/ML and blockchain, to optimize the supply chain life cycle from start to finish. 08:31 Adopting a multicloud strategy is a big step towards future-proofing your business and we’re here to help you navigate this complex landscape. With our suite of courses, you'll gain insights into network connectivity, security protocols, and the considerations of working across different cloud platforms. Start your journey today to multicloud today by visiting mylearn.oracle.com. 08:58 Nikita: Welcome back! Sarah, what makes Oracle Fusion SCM so powerful? Sarah: When it comes to planning, you can leverage strategic, tactical, and operational processes for accurate forecasting and resource alignment. Sourcing and manufacturing help you streamline procurement and production to meet supply and demand efficiently. Inventory and warehousing processes ensure the right goods are available, stored, and managed effectively. Fulfillment is also known as the pick, pack, and ship part of the supply chain and delivery entails order tracking and receipt. Having connected processes in place ensures that billing and revenue recognition are applied correctly on the goods and services. Great customer service models provide accurate tracking of customer orders and deliveries. And this can provide insight for an accurate picture of future planning, manufacturing, and inventory forecasts. This is a constant cycle because information and analytics feed into the planning process. Oracle Supply Chain Management is designed to seamlessly integrate and optimize every step of the supply chain process, ensuring businesses can adapt to dynamic market conditions and customer expectations. The solution supports end-to-end supply chain processes and leverages cutting-edge technologies to transform how organizations manage their operations. In planning, Oracle SCM empowers businesses with advanced planning tools to align supply and demand effectively. Sourcing and manufacturing assists in streamlining procurement and manufacturing workflows to drive efficiency. Inventory and warehousing optimizes inventory and warehouse management processes with intelligent capabilities. Fulfillment delivery helps to accelerate order fulfillment and delivery operations to meet customer needs. And servicing allows you to maintain strong customer relationships through seamless post-sale servicing. Oracle SCM ensures an agile and resilient supply chain with the help of technologies like AI, ML, and blockchain. These tools empower organizations to stay competitive in a fast-paced environment while exceeding customer expectations. 11:03 Lois: To round out our discussion, let’s talk about Oracle Customer Experience. Sarah: Customer Experience, or CX, provides the platform and products necessary to capture all customer touch points and interactions. This platform also automates the business process from interest and lead generation to the sale and provision of products and services. The major product areas are marketing, sales, service, and CX platform. 11:32 Nikita: Could you dive a bit deeper into its key areas? Sarah: Oracle Marketing solutions allow you to create targeted cross-channel marketing campaigns, optimize lead generation activities, personalize customer and prospect communication, and automate marketing activities. Use real-time data-driven insights to engage, convert, and nurture buyer relationships to increase sales. Featured products include Eloqua Marketing Automation, Responsys Campaign Management, CrowdTwist Loyalty and Engagement, Infinity Behavioral Intelligence, Unity Customer Data Platform, and more. With Oracle Sales, you can deliver responsive selling across all touchpoints. Oracle Sales guides sellers with intelligent recommendations and gives them a faster path to critical records to help them focus on the right prospects at the right time. The modern, unified selling and buying approach of Oracle CX connects sales and commerce to service, marketing, and the entire customer experience. Featured products include Salesforce Automation, Sales Planning, Sales Performance Management, Configure, Price, and Quote, Subscription Management, Partner Relationship Management, and Customer Data Management. Oracle Service enables you to help customers when and where they need you with automated workflows for customer self-service, agent-assisted service, and Field Service engagements. You can accelerate the resolution of service issues with AI-driven recommendations, unified data visibility, and cross-organization and cross-channel collaboration tools. At Oracle, we make every customer interaction matter by using a suite of CX Cloud applications that connect marketing, sales, customer service, Field Service, and e-commerce. Oracle connects our customer experience systems with finance, supply chain, and HR on a unified cloud platform for a single, dynamic 360-degree view of the customer. 13:31 Lois: Before we wrap up, how does Oracle Modern Best Practice, or OMBP, fit into Oracle Cloud Apps? Sarah: OMBP illustrates common business processes optimized to leverage the latest applications and technologies in Oracle Fusion Applications. Oracle Modern Best Practice comprises reimagined industry standard business processes powered by Oracle technology. Engineered into Fusion Applications, OMBP simplifies and streamlines workflows, enabling organizations to leverage modern, efficient, and scalable practices. As we align more assets with OMBP, there will be a stronger connection between global process owners and business process innovation within a customer's organization. OMBP was derived from over 10,000 successful delivery projects. To publish an OMBP, past Oracle projects were analyzed for successful and unsuccessful processes. Successful processes were reviewed and optimized by product experts, engineers, customers, and key users. Optimized processes were published to OMBP to make them available to other customers. 14:40 Lois: Well, that’s it for this episode. Thank you, Sarah, for all of your incredible insights. Nikita: If you want to learn more about what we discussed today, head over to mylearn.oracle.com and take a look at the Oracle Fusion Cloud Applications Process Essentials courses. Until next time, this is Nikita Abraham… Lois: And Lois Houston, signing off! 15:01 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
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Modernize Your Business with Oracle Cloud Apps – Part 1
07/22/2025
Modernize Your Business with Oracle Cloud Apps – Part 1
Join hosts Lois Houston and Nikita Abraham, along with Cloud Delivery Lead Sarah Mahalik, as they unpack the core pillars of Oracle Fusion Cloud Applications—ERP, HCM, SCM, and CX. Learn how Oracle’s SaaS model, Redwood UX, and built-in AI are reshaping business productivity, adaptability, and user experience. From quarterly updates to advanced AI agents, discover how Oracle delivers agility, lower costs, and smarter decision-making across departments. Oracle Fusion Cloud Applications: Process Essentials Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. -------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Nikita: Welcome to the Oracle University Podcast! I’m Nikita Abraham, Team Lead: Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi everyone! In our last two episodes, we explored the Oracle Cloud Success Navigator platform. This week and next, we’re diving into Oracle Fusion Cloud Applications with Sarah Mahalik, a Cloud Delivery Lead here at Oracle. We’ll ask Sarah about Oracle’s cloud apps suite, the Redwood design system, and also look at some of Oracle’s AI capabilities. 01:02 Nikita: Yeah, let’s jump right in. Hi Sarah! How does Oracle approach the SaaS model? Sarah: Oracle's Cloud Applications suite is a complete enterprise cloud designed to modernize your business. Our cloud suite of SaaS applications, which includes Enterprise Resource Planning, or ERP, Supply Chain Management, or SCM, Human Capital Management, or HCM, and Customer Experience, or CX, brings consistent processes and a single source of truth across the most important business functions. At Oracle, we own all of the technology stacks that power our suite of cloud applications. Oracle Cloud Applications are built on Oracle Cloud Infrastructure and ensure the performance, resiliency, and security that enterprises need. Your business no longer needs to worry about maintaining a data center, hardware, operating systems, database, network, or all of the security. With deep integrations, a common data model, and a unified user interface, these applications help improve customer engagement, increase agility, and accelerate response to change. Oracle's Cloud Applications are updated quarterly with new features and improvements. These updates are based on our deep understanding of customer's functional needs, as well as modern technologies such as artificial intelligence, machine learning, blockchain, and digital assistants. Expectations for user experience only go up. Oracle's Redwood User Experience methodology ensures those expectations are matched and exceeded by including powerful and predictive search, a look and feel that actually helps users see what they need to in the order they need to see it, and by providing conversational and micro-interactions. Oracle, as a SaaS provider, puts the customer first by having enough dedicated resources to ensure zero downtime and increasing the speed of implementation by eliminating much of the hardware and software setup activity. 02:59 Nikita: What are the advantages of adopting Oracle Cloud Apps? Sarah: First off, Oracle provides automatic quarterly updates, and they're usable immediately. Customers can focus on leveraging the new functionality instead of spending cycles on installing it. There's much more accessibility because Oracle hosts the heavy part of the applications and customers access it via thin clients. The applications can be used from nearly anywhere and on a wide range of devices, including smartphones and tablets. Another great advantage is speed and agility. A lot of the benefits you see here result from Oracle's provider model. That means customers aren't spending time on customization, application testing, and report development. Instead, they work on the much lighter and faster tasks of configuration, validation, and leveraging embedded analytics. And finally, it's just better economics. Because of the pricing model, it is easy to compare an on-premises implementation cost. While upfront costs are almost always lower, overall operational costs and risk are usually lower as well. This translates to better total cost of ownership and improved overall economics and agility for your business. 04:10 Lois: Sarah, in your experience, why do customers love Oracle Cloud Apps? Sarah: At Oracle, we empower you with embedded AI that drives real breakthroughs in productivity and efficiency, helping you stay ahead of the curve. With the power of Oracle Cloud Infrastructure, you get the best of performance, security, and scalability, making it the perfect foundation for your business. Our modern user experience is intuitive and designed with your needs in mind, while our relentless innovation is focused on what truly matters to you. Above all, our commitment to your success is unwavering. We're here to support you every step of the way, ensuring you thrive and grow with Oracle. 04:49 Lois: Let’s talk about Oracle’s Redwood design system. What is it? And how does it enhance the user experience? Sarah: Redwood is the name of Oracle's next-generation user experience. Redwood design system is a collection of prefabricated components, templates, and patterns to enable developers to quickly create very sophisticated and polished interactions that are upgrade safe. It provides a consumer grade-plus experience, where you have high-quality functionality that can be used across multiple devices. You have access to insightful data readily at your fingertips for quick access and decision making, with the option to personalize your application to create your own state of the art experience. Processes and entry time will now be more efficient and streamlined by having fewer clicks and faster downloads, which will lead to high productivity in areas that matter the most. The Redwood design is intelligent, meaning you have access to AI, where you will receive recommendations and guidance based on your preferences and business processes. It's also adaptable, allowing you to use the same tools to create new experiences by using the Business Rule Framework with modern UX components. Oracle's Redwood user experience will help you to be more productive, efficient, and engaged with a highly personalized experience. 06:11 Are you keen to stay ahead in today's fast-paced world? We’ve got your back! Each quarter, Oracle rolls out game-changing updates to its Fusion Cloud Applications. And to make sure you’re always in the know, we offer New Features courses that give you an insider’s look at all of the latest advancements. Don't miss out! Head over to mylearn.oracle.com to get started. 06:37 Nikita: Welcome back! Sarah, you said the Redwood design system is adaptable. Can you elaborate on what you mean by that? Sarah: In a nutshell, this means that developers can extend their applications using the same development platform that Oracle Cloud Applications are built on. Oracle Visual Builder Studio is a robust application development platform that enables users to rapidly create and extend web, mobile, and progressive web interfaces using a visual development environment. It streamlines application development and reduces coding, while also providing flexibility and support for popular build and testing frameworks. With Oracle Visual Builder Studio, users can build apps for the web, create progressive web apps, and develop on-device mobile apps. The tool also offers access to REST services and allows for planning and managing development processes, as well as managing the code lifecycle. Additionally, Oracle Visual Builder Studio provides hosting for apps along with easy publishing and version management. Changes made using Visual Builder Studio are called Application Extensions. Visual Builder Studio Express Mode has two key components: Business Rules and Constants. Use Business Rules, which is the Redwood equivalent to Transaction Design Studio for responsive pages, to leverage delivered best practices or create your own rules based on various criteria, such as country and business unit. Make fields and regions required or optional, read-only or editable, and show or hide fields in regions, depending on specific criteria. Use the various delivered Constants to customize your Redwood pages to best fit your specific business needs, such as hide the evaluation panel and connections or reorder the columns in the person search result table. 08:23 Lois: Sarah, here’s a question that's probably on everyone's mind—what about AI for Fusion Applications? Sarah: Oracle integrates AI into Fusion Applications, enabling faster, better decision making and empowering your workforce. With both classic and generative AI embedded, customers can access AI-driven insights seamlessly within their everyday software environment. In HCM, AI helps to automate routine tasks. It's also used to attract and manage talent more efficiently by doing things like reducing the time to hire and performing automatic skill matching for job vacancies. It also uses some of that skill matching for existing employees to optimize their engagement, improve productivity, and maximize career growth. All the while, it provides suggested actions so that those tasks are quick, accurate, and easy. In SCM, AI helps predict order cycle times by analyzing historical data and trends, allowing for more accurate planning. It also generates item descriptions automatically and uncovers potential suppliers by analyzing market data, thereby improving efficiency and sourcing decisions. With ERP, it's all about delivering efficiencies and improving strategic contributions. You can use AI to automate some of the core processes and provide guided actions for users in the rest of the processes. This is our recipe for improved efficiency and reduced human error. For example, in Payables, you can use AI features to accelerate and simplify invoice processing and identify duplicate transactions. And in CX, AI helps you to identify which sales leads offer the greatest potential, provides real-time news alerts, and then recommends actions to ensure that reps are working on the right opportunity at the right time and improving conversion to sale. 10:11 Lois: Everyone’s heard about AI agents, but I’ve always wondered how they work. Sarah: AI agents are a combination of large language models and other advanced technologies that interact with their environments, automate complex tasks, and collaborate with employees in real time. Reasoning capabilities in these LLMs differentiate AI agents from the brittle rules-based automation of the past. Since they can make judgment calls, AI agents can create action plans and manage workflows, either independently or with human supervision. At the core of their functionality is the capability to learn from previous interactions, use data from internal systems, and collaborate with both people and other agents. This ability to continuously adapt makes AI agents particularly valuable for complex business environments, where flexibility and scalability are key. 11:01 Nikita: And how do they work specifically in Fusion Apps? Sarah: Oracle Fusion AI agents are autonomous assistants designed to help organizations streamline operations, improve decision making, and reduce manual workloads. They can assist with simple or complex tasks and work across departments. 11:19 Lois: Sarah, what are the different types of AI agents? Sarah: Functional agents act as digital assistants for different personas within the enterprise and perform domain-specific tasks. Supervisory agents manage other agents, overseeing complex workflows and making decisions on whether human intervention is needed. Utility agents perform routine low-risk tasks such as retrieving data, sending notifications, or running reports. They're often optimized to help with specific roles, so an AI agent or collection of agents might act as a finance clerk, hiring manager, or a customer service representative. 11:54 Nikita: Can you give us some real-world use cases? Sarah: In human resources, agents will assist employees with benefit inquiries and policy clarifications. In finance, agents will automate invoice approvals and help optimize financial workflows. In supply chain management, a field service agent can guide technicians through repairs by providing real-time diagnostic data, troubleshooting steps, and automating orders for parts. In customer experience, the contracts researcher agent enables sales teams to automate routine contract workflows and approvals so they can focus on selling rather than administrative tasks. Oracle Fusion AI agents represent a leap beyond traditional AI. They don't just automate, they collaborate with human workers, making AI agents more than just tools. By integrating advanced AI within business systems, Oracle continues to lead the way in improving productivity and operational efficiency. As AI technology evolves, expect to see even more sophisticated AI agents capable of managing entire business processes autonomously, giving your team the freedom to focus on strategic, high-impact activities. 13:04 Nikita: Thank you so much for taking us through all that, Sarah. We’re really excited to have you back next week to continue this discussion. Lois: And if you liked what you heard today, head over to mylearn.oracle.com and take a look at the free Oracle Fusion Cloud Applications Process Essentials courses to learn more. Until next time, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 13:27 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
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Oracle Cloud Success Navigator – Part 2
07/15/2025
Oracle Cloud Success Navigator – Part 2
Hosts Lois Houston and Nikita Abraham continue their discussion with Mitchell Flinn, VP of Program Management for the CSS Platform, by exploring how Oracle Cloud Success Navigator helps teams align faster, reduce risk, and drive value. Learn how built-in quality benchmarks, modern best practices, and Starter Configuration tools accelerate cloud adoption, and explore ways to stay ahead with a mindset of continuous innovation. Oracle Cloud Success Navigator Essentials: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ----------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University and with joining me today is Nikita Abraham, Team Lead of Editorial Services. Nikita: Hi everyone! In our last episode, we gave you a broad overview of the Oracle Cloud Success Navigator platform—what it is, how it works, and its key features and benefits. Today, we’re continuing that discussion with Mitchell Flinn. Mitchell is VP of Program Management for Oracle Cloud Success Navigator, and in this episode, we’re going to ask him to walk us through some of the core components of the platform that we couldn’t get into last week. 01:04 Lois: Right, Niki. Hi Mitchell! You spoke a little about Cloud Quality Standards in our last episode. But how do they contribute or align with the vision of Oracle Cloud Success Navigator? Mitchell: The vision for Navigator is to support customers throughout every phase of their cloud journey, providing timely advice to help improve outcomes to reduce cost and increase overall value. This model is driven through Oracle Cloud Quality Standards. These standards are intended to improve the transparency and collaboration between customer, partner, and Oracle members of a project. This is a project blueprint to include the ability for business and IT users to align on project coordination, expectations, and ultimately drive tighter alignment. Tracking key milestones and activities can help visualize and measure progress. You can build assessments and help answer questions so that at the right time, you have the right resources to make the right decisions for an organization. Cloud Quality Standards represent the key milestone dates and accomplishments along the journey. You can leverage these to increase project transparency, reduce risk, and increase the overall collaboration. Cloud Quality Standards are proactive list of must haves leveraged by customers, partners, and Oracle. They're a collection of knowledge and lessons learned from thousands of implementations globally. Cloud Quality Standards are partner agnostic and complimentary to all SI methodologies and tool sets. And they've been identified to address delivery issues before they happen and reduce the risk of implementations. 02:34 Lois: Ok, and a crucial component of Oracle Cloud Success Navigator is Oracle Modern Best Practice, or OMBP, right? Can you tell us more about what this is? Mitchell: Oracle Modern Best Practices are based on distilled knowledge of our customers' needs gained from 10,000 successful delivery projects. They illustrate the business process components and their optimization to take advantage of the latest Oracle applications and technologies. Oracle Modern Best Practices comprise industry best practices and processes powered by Oracle technology. Engineered in Fusion Applications, OMBPs simplify and streamline workflows. They enable organizations to leverage modern, efficient, and scalable practices. As we align our assets with OMBPs, there's a stronger connection between global process owners and business process innovation within a customer's organization. 03:21 Nikita: And how do they help deliver end-to-end success for businesses? Mitchell: An OMBP approach involves a digital business process, so evolving and adapting in real time to changing market dynamics. End-to-end across the organization, so we're breaking down silos and ensuring there's operational agility and a seamless collaboration between departments. We're leveraging emerging technologies, so utilizing AI, other cutting-edge technologies to automate routine tasks, enabling greater human creativity and unlocking new value and insights. And radically superior results, driving a significant improvement in measurable outcomes. OMBPs are dynamic, and when regularly updated, they meet evolving customer needs and technologies. They're trusted, tested, and validated by Oracle experts and publicly available and download on oracle.com. If you go to oracle.com and search modern best practice, you'll find more detailed introduction to Oracle Modern Best Practices. You'll also find Oracle Modern Best Practice business processes for domains such as ERP, EPM, Supply Chain, HCM, and Customer Experience. We also have Oracle Modern Best Practices for specific industries. 04:25 Nikita: What are the key benefits of OMBP? Mitchell: Revolutionary new technologies are available for organizations and business leaders. You might wonder how existing business processes are optimized with old technology and how they can drive the best solution. With more emerging technologies reaching commercial availability, existing best practices become outdated. And to stay competitive, organizations need to continuously innovate and incorporate new technology within their best practices. In Oracle's definition of OMBPs, common business processes are considered historic input, but we also factor in what could be done with new technologies. And based on this approach, Oracle Modern Best Practices help us evolve with the organizational needs as market dynamics change, work end to end across organizations to eliminate department silos and ensure agility. It allows us to use technologies such as AI to automate the mundane and unlock human creativity for new value and insight. This allows us to incorporate next generation digital technologies to enable radically superior, measurable results. To achieve these, Oracle makes use of key differentiators such as analytics and AI and machine learning. Analytics are also known as business intelligence provides you with information in the form of pre-built dashboards, showing your key metrics in real time. Embedded analytic capabilities enable you to monitor business performance and make better decisions. 05:44 Lois: And what about AI and machine learning? Mitchell: These focus on building systems that learn or improve performance based on the data that they consume. Smart digital assistants, recommendation engines, predictive analytics, they're all used within AI and machine learning to help organizations automate operations and drive innovation, and ultimately make better decisions faster. 06:02 Nikita: Mitchell, let’s move on to the Starter Configuration. Can you explain what it is and how it helps during a cloud implementation? Mitchell: Starter Configuration is a predefined configuration of Oracle Cloud Applications aligned with the Oracle Modern Best Practices. It's very comprehensive and includes business processes in several domains, such as ERP, HCM, Supply Chain, EPM, and so on. It includes sample, master, and transactional data, and predetermined usernames, which aligns and tests based on the same use cases you saw in Oracle Modern Best Practices in Cloud Success Navigator. Customers can request deployment of a Starter Configuration into their test environment. Oracle will run an automated process for replicating the configuration, master data, transaction data, and predetermined usernames from Oracle to the Oracle Cloud Applications Test Environment of the customer's choice. For best user experience, customers can add a basic level of personalization, such as their customer name, limited number of employees, suppliers, customers, and a few other items. Starter Configuration’s delivered with predetermined step guides for comprehensive set of use cases. Using these, customers can relay the same use cases they've seen in Oracle Modern Best Practices and Success Navigator. In the Oracle Cloud Applications Test Environment Customer, we've been able to enable an in-app guidance using Oracle Guided Learning. This helps to make it easier for navigation through the business processes supported by the application. Oracle can deploy the Starter Configuration in days, not weeks or months, which means the implementation partners don't need to invest time and effort for the first configuration of an Oracle Cloud Application environment before they can even get the chance to show it to a customer. In turn, once Starter Configuration is deployed, it's ready to be used for solution familiarization and design activities. Using Starter Configuration of Oracle Cloud Applications early in the cloud journey will offer several benefits to customers. 08:00 Lois: What are these benefits? Mitchell: The first, it helps to cut down on environment configuration time from several weeks or months to potentially just days. Next, implementation partners can engage stakeholders early, and get them familiar with Oracle Cloud Applications, especially those that maybe have never participated in the sales cycle. Because customer stakeholders actually see what Oracle Cloud solutions might look like in the future, it becomes easier to take design decisions. Starter Configuration provides hands-on familiarization with Oracle Cloud Applications and Oracle Leading Practices. This makes it easier to understand what leading practices and standard features can be adopted to support future business processes. It also reduces the level of customization and accelerates implementation. 08:45 Transform the way you work with Oracle Database 23ai! This cutting-edge technology brings the power of AI directly to your data, making it easier to build powerful applications and manage critical workloads. Want to learn more about Database 23ai? Visit mylearn.oracle.com to pick from our range of courses and enroll today! 09:10 Nikita: Welcome back! Mitchell, how can customers and implementation partners best use the Starter Configuration? Mitchell: Customers and implementation partners will work in close collaboration to make the implementation successful. Hence, Oracle recommends that customers and implementation partner discuss how the best use of Starter Configuration will take place. This is one of the key activities in the mobilize stage of the cloud journey. First, Oracle recommends to use Starter Configuration to prepare the customer stakeholders for the project. Customer stakeholders who participate in the project should go to the Oracle Modern Best Practice section of Success Navigator platform in order to learn more about the modern best practices, business processes, personas, leading practices, and use cases. Project team can request Starter Configuration early in the project to allow customer stakeholders to get their hands-on experience with performing use cases in the Starter Configuration. Customer stakeholders will perform use cases in Starter Config to learn more about modern best practices. They'll use the step-by-step guides and Guided Learning to easily perform the use cases within the Starter Configuration. This is how they'll visualize use cases in Oracle Cloud Applications and get a good understanding of Oracle Modern Best Practices. Next, mobilize stage of the journey, project team can use Starter Configuration to visualize the solution and make design decisions with confidence. First, by requesting Starter Configuration, implementation partners can engage stakeholders early and create the space to get familiar with Oracle Applications. This applies especially to those that may have not participated during the sales cycle. You could personalize Starter Configuration to enhance the user experience to help the customer connect to the application and, for example, change the company name, the logo, few supplier names, customer names, employee names, etc. And implementation partners are going to be able to run sessions to familiarize the customer with modern best practices and show how cloud applications support use cases. For structured guidance, the implementation partners can use the step guides. It includes screenshots of OGL within cloud applications environments. And you could run design workshop and use Starter Configuration, show and explain which design decisions must take place to define a customer-centric configuration. Finally, you can show use cases that help you explain what the impact of design decisions might be. 11:20 Lois: Mitchell, before we wrap up, can you take us through how the Release Readiness features facilitate innovation? Mitchell: In order to innovate with the Release Readiness features, it's important to learn about the new features in a one-stop shop, and then connect with the capability. The first item is to be able to find and familiarize yourself with the content as they exist within Release Notes. From there, it's important to be able to actually experience those items by looking at the text, and pictures, and the Oracle University videos that we provide in the Feature Overviews, as well as additional capabilities that will be coming with the Navigator in the preview environment, your ability to get your hands on in a demo experience through Cloud Success Navigator. Furthermore, it's important for you to be able to explore across theme-based items, which we call Adoption Centers, currently ready for AI in Redwood. This gives you the ability to span across Release Notes and different releases in order to understand the themes of the trends around AI and Redwood, and how those capabilities in our technology can advance your innovation in the Cloud. And finally, you need to be able to understand those opportunities based off of business processes, data insights, and industry benchmarks. That way, you can understand the capabilities as they exist, not just for your business specifically, but in the context of the broader industry and technology trends. From there, it's important for you to then think about your ability to collaborate to drive continuous innovation. We want to be able to leverage Cloud Success Navigator to drive collaboration to increase confidence across all members of the project team, whether it be you as a customer, our partners, or the Oracle team. It should also be able to drive an increased efficiency within decision making, driving greater value and readiness as you think about the proposed adoption changes. Finally, we want to think about the ability to reduce cycles related to features and decisions so that you can more quickly adapt, and adjust, and consume innovations as they're produced on a quarterly basis. 13:09 Nikita: I think we can end with that. Thank you so much, Mitchell, for taking us through the Navigator platform. Lois: And if you liked what you heard today, head over to mylearn.oracle.com and take a look at the Oracle Cloud Success Navigator Essentials course to learn more. It’s available for free! Until next time, this is Lois Houston…. Nikita: And Nikita Abraham, signing off! 13:33 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
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Oracle Cloud Success Navigator – Part 1
07/08/2025
Oracle Cloud Success Navigator – Part 1
In this episode of the Oracle University Podcast, Lois Houston and Nikita Abraham are joined by Mitchell Flinn, VP of Program Management for the CSS Platform, to explore Oracle Cloud Success Navigator. This interactive platform is designed to help customers optimize their cloud journey, offering best practices, AI tools, and personalized guidance from implementation to innovation. Don’t miss this insider look at maximizing your Oracle Cloud investment! Oracle Cloud Success Navigator Essentials: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ---------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Nikita: Welcome to the Oracle University Podcast! I’m Nikita Abraham, Team Lead of Editorial Services with Oracle University, and joining me is my co-host Lois Houston, Director of Innovation Programs. Lois: Hi everyone! Today is the first of a two-part special on Oracle Cloud Success Navigator. This is a tool that provides you with a clear path to cloud transformation and helps you get the most out of your cloud investment. 00:52 Nikita: And to tell us more about this, we have Mitchell Flinn joining us today. Mitchell is VP of Program Management for Oracle Cloud Success Navigator. In this episode, we’ll ask Mitchell about the ins and outs of this powerful platform, its benefits, key features, and the role it plays in streamlining cloud journeys. Lois: Yeah. Hi Mitchell! What is Oracle's approach to cloud technology and customer success, and how does the Cloud Success Navigator support this philosophy? 01:22 Mitchell: Oracle has an amazing amount of industry-leading enterprise cloud technologies across our entire portfolio. All of this is at your disposal. That, coupled with the sole focus of your success, forms the crux of the company's transformational journey. In other words, we put your success at the heart of everything we do. For each organization, the path to achieve maximum value from our technology is unique. Success Navigator reflects our emphasis on being there with you throughout the entire journey to steer you to success. 01:53 Nikita: Ok, what about from a business’s viewpoint? Why would they need the Navigator? Mitchell: Businesses across every industry are moving mission-critical applications to the cloud. However, business leaders understand that there's no one-size-fits-all model for cloud development and deployment. Some fundamentals for success are your need to ensure new technologies are seamlessly integrated into day-to-day operations and continually optimize to align with evolving business requirements. You must ensure stakeholder visibility through the journey with updates at every stage. Building system efficiencies into other key tasks, which has to be done at the forefront when considering your cloud transformation. You also need to quickly identify risks and address them during the implementation process and beyond. Beyond the technical execution, cloud deployments also require significant process and organizational changes to ensure that adoption is aligned with business goals and delivers tangible benefits. Moreover, the training process for new features after cloud adoption can be an organization wide initiative that needs special attention. These requirements or more can be addressed through Oracle Cloud Success Navigator, which is a new interactive digital platform to guide you through all stages of your cloud journey. 03:09 Lois: Mitchell, how does Cloud Success Navigator platform enhance the user experience? How does it support customers at different stages of their cloud journey? Mitchell: Platform is included for free for all cloud application customers. And core to Success Navigator is the goal of increasing transparency among customers, partners in the Oracle team, from project kickoff through quarterly releases. Included in the platform are implementation best practices, Oracle Modern Best Practices focused on solutions provided by our applications, and guidance on living within the cloud. Success Navigator supports you for every stage of your Oracle journey. You can first get your bearings and understand what's possible with your cloud solution using preconfigured starter environments to support your design decisions. It helps you chart a proven course by providing access to Oracle expertise and Oracle Modern Best Practices, so you can use cloud quality standards to guide your implementation approach. You can find value from quarterly releases using AI assistants and preview environments to experience and adopt latest features that matter to you. And you can blaze new trails by building your own cloud roadmap based on your organization's goals, keeping you focused on the capabilities you need for day-to-day and the road ahead. 04:24 Nikita: How does the Navigator cater to the needs of all the different customers? Mitchell: For customers just getting started with Oracle implementations, Navigator provides a framework with success criteria for each stakeholder involved in the implementation, and provides recommended milestones and checklists to keep everyone on track. For existing customers and experienced cloud customers thriving in the cloud, it provides contextually relevant insights based on your cloud landscape. It prepares you for quarterly releases and preview environments, and enables the use of AI and optimization within your cloud investment. For our partners, it allows Oracle to work in a collaborative way to really team up for our customers. Navigator gives transparency to all stakeholders and helps determine what success criteria we should be thinking about at each milestone phase of the journey. And it also helps customers and partners get more out of their Oracle investment through a seamless process. 05:20 Lois: Right. Mitchell, can you elaborate on the use cases of the platform? How does it address challenges and requirements during cloud implementations? Mitchell: We can create transparency and alignment between you, your partner, and the Oracle team using shared view of progress measured through standard criteria. We can incorporate recommended key milestones and activities to help you visualize and measure your progress. And we can use built-in assessments, remove risk, and ask the right questions at the right time to make the right implementation decisions for your organization. Additionally, we can use Starter Configuration, which allows you to experience the latest capabilities and leading practices to enrich design decisions for your organization with Starter Configuration. This can activate Starter Configuration early in your journey to understand what delivered capability can do for you. It allows you to evaluate modern best practices to determine how your design process can work in the future. And it empowers and educates your organization by interacting with real capability, using real processes to make the right decisions for your cloud implementation. You're able to access new features in Fusion updates. You can do this to learn what you need to know about new features in a one-stop shop and connect your company in a compelling capacity. You can find, familiarize, and prioritize new and existing features in one place. And you can experience new features in hands-on preview environments available with you with each quarterly release. You can explore new theme-based approaches using adoption centers for AI and Redwood to understand where to start and how to get there. And you can understand innovation opportunities based on business processes, data insights, and industry benchmarks. 07:01 Nikita: Now that we’ve covered the basics, can you walk us through some of the key features of the platform? Let’s start with the Home page. Mitchell: This is the starting point of the customer journey and the central location for everything Navigator has to offer, including best practice content. You'll find content focused on implementation phase, the innovation phase, and administrative elements like the team structure, program and projects, and other relevant tips and tricks. Cloud Quality Standards provides learning content and checklists for successful business transformation. This helps support the effective adoption and adherence to Cloud Quality Standards and enables individuals to leverage AI and predictive insights. The feature Sunburst allows capability for features to be reviewed in an interactive graphic, illustrating new features by pillar, other attributes, which enable customers to review features curated to identify and adopt new features that meet their needs. It helps you understand recommended features across your application base based off of a production profile, covering mandatory adoption requirements, efficiency gains, innovation capabilities like AI and Redwood to drive business change. Next is the Adoption Center, which addresses the need of our existing and implementing customers. It covers the concept of how Redwood is an imperative for our application customers, what it means, and how, and when we could translate some of the requirements to a business user or an IT user. Roadmap is an opportunity for the team to evaluate which features are most interesting at any given moment, the items that they would like to adopt next, and save features or items that they might require later. 08:36 Lois: That’s great. Mitchell, I know we have two new features rolling out in 2025. Can you tell us a little bit about them? Mitchell: Preview Environment. This allows users to explore new features and orderly release through a shared environment by logging into Navigator, eliminating potential regression needs, data adjustments and loads, and other common pre-work. You can explore the feature directly with the support of Oracle Guided Learning. The second additional feature for 2025 is AI Assist. We've taken everything within Navigator and trained an LLM to provide customers the ability to inquire about best practices, solutions, and features within the applications and ultimately make them smarter as they prepare for areas like design workshops, quarterly release readiness, and engaging across our overall Oracle team. Customers can use, share, and apply Oracle content knowledge to their day-to-day responsibilities. 09:32 Have you mastered the basics of AI? Are you ready to take your skills to the next level? Unlock the potential of advanced AI with our OCI Generative AI Professional course and certification that covers topics like Large Language Models, the OCI Generative AI Service, and building Q&A chatbots for real-world applications. Head over to mylearn.oracle.com and find out more. 10:01 Nikita: Welcome back! Mitchell, how can I get started with the Navigator? Mitchell: To request customer access to Success Navigator's environment, you need to submit a request form via the Success Navigator page on oracle.com. You need to be able to provide your customer support identifier with the help icon around the CSI number. If you don't have your CSI number, you can still submit your request and a member of the Success Navigator team will reach out and coordinate with you to understand the information that's required. Once access is granted, you'll receive a welcome email to Oracle Cloud Success Navigator. 10:35 Lois: Alright, and after that’s done? Mitchell: Before implementing Oracle Cloud Applications in your organization, you need to think of a structure that helps you organize and manage that implementation. To implement a solution footprint for a relatively small organization operating in a single country, and with an implementation time of only a few months, defining a single project might be a good enough organization to manage the implementation. For example, if you're implementing Core Human Capital Management applications, you could define a single project for that. But if you're implementing a broad solution footprint for a large business across multiple organizational entities in multiple countries, and with an implementation time of several years, a single project isn't enough. In such cases, organizations typically define a structure of a program with multiple underlying projects that reflect the scope, approach, timelines of business transformation. For example, you're implementing both HCM and ERP applications in a two-phased approach. You might decide to define a program with two underlying projects, one for HCM and one for ERP. For large, international business transformations, you can define multiple programs, each with multiple underlying projects. For example, you might first need to define a global design for a common way of working with HCM and then start rolling out that global design to individual countries. In such a scenario, you might define a program called HCM Transformation with a first project for global design, followed by multiple projects associated with individual countries. You could do the same for ERP. 12:16 Nikita: Mitchell, we’ve mentioned “cloud journey” a few times in this conversation, but can you tell us a little more about it? How can teams use it to guide their work? Mitchell: The main purpose of Oracle's cloud journey is to provide leading practices and actionable guidance to customers and implementation partners for successful implementations, operations within the cloud, and innovation through Oracle Cloud solutions. These are defined in terms of stages, activities, topics, and milestones. They focus on, how do you do the right things at the right time the first time. For customers, implementation partners in the cloud journey, this allows us to facilitate collaboration and transparency, accelerate the journey, and visualize and measure our progress. 12:59 Lois: We’ll stop here for today. Join us next week as we continue our discussion on Oracle Cloud Success Navigator. Nikita: And if want to look at some demos of everything we touched upon today, head over to mylearn.oracle.com and take a look at the Oracle Cloud Success Navigator Essentials course. Until next time, this is Nikita Abraham… Lois: And Lois Houston, signing off! 13:22 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
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Oracle GoldenGate 23ai: Parameters, Data Selection, Filtering, & Transformation
07/01/2025
Oracle GoldenGate 23ai: Parameters, Data Selection, Filtering, & Transformation
In the final episode of this series on Oracle GoldenGate 23ai, Lois Houston and Nikita Abraham welcome back Nick Wagner, Senior Director of Product Management for GoldenGate, to discuss how parameters shape data replication. This episode covers parameter files, data selection, filtering, and transformation, providing essential insights for managing GoldenGate deployments. Oracle GoldenGate 23ai: Fundamentals: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. --------------------------------------------------------------- Podcast Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hi everyone! This is the last episode in our Oracle GoldenGate 23ai series. Previously, we looked at how you can manage Extract Trails and Files. If you missed that episode, do go back and give it a listen. 00:50 Lois: Today, Nick Wagner, Senior Director of Product Management for GoldenGate, is back on the podcast to tell us about parameters, data selection, filtering, and transformation. These are key components of GoldenGate because they allow us to control what data is replicated, how it's transformed, and where it's sent. Hi Nick! Thanks for joining us again. So, what are the different types of parameter files? Nick: We have a GLOBALS parameter file and your runtime parameter files. The global one is going to affect all processes within a deployment. It's going to be things like where's your checkpoint table located in name, things like the heartbeat table. You want to have a single one of these across your entire deployment, so it makes sense to keep it within a single file. We also have runtime parameter files. This are going to be associated with a specific extract or replicat process. These files are located in your OGG_ETC_HOME/conf/ogg. The GLOBALS file is just simply named GLOBALS and all capitals, and your parameter file names for the processes themselves are named with the process.prm. So if my extract process is EXT demo, my parameter file name will be extdemo.prm. When you make changes to parameter files, they don't take effect until the process is restarted. So in the case of a GLOBALS parameter file, you need to restart the administration service. And in a runtime parameter file, you need to restart that specific process before any changes will take effect. We also have what we call a managed process setting profile. And this allows you to set up auto restart profiles for each process. And the GoldenGate Gate classic architecture, this was contained within the GLOBALS parameter file and handled by the manager. And microservices is a little bit different, it's handled by the service manager itself. But now we actually set up profiles. 02:41 Nikita: Ok, so what can you tell us about the extract parameter file specifically? Nick: There's a couple things within the extract parameter file is common use. First, we want to tell what the group name is. So in this case, it would be our extract name. We need to put in information on where the extract process is going to be writing the data it captures to and that would be our trail files, and extract process can write to one or more trail files. We also want to list out the list of tables and schemas that we're going to be capturing, as well as any kind of DDL changes. If we're doing an initial load, we want to set up the SQL predicate to determine which tables are being captured and put a WHERE clause on those to speed up performance. We can also do filtering within the extract process as well. So we write just the information that we need to the trail file. 03:27 Nikita: And what are the common parameters within an extract process? Nick: There are a couple of common parameters within your extract process. We have table to list out the list of tables that GoldenGate is going to be capturing from. These can be wildcarded. So I can simply do table.star and GoldenGate will capture all the tables in that database. I can also do schema.star and it will capture all the tables within a schema. We have our EXTTRAIL command, which tells GoldenGate which trail to write to. If I want to filter out certain rows and columns, I can use the filter cols and cols except parameter. GoldenGate can also capture sequence changes. So we would use the sequence parameter. And then we can also set some high-level database options for GoldenGate that affect all the tables and that's configured using the tranlog options parameter. 04:14 Lois: Nick, can you talk a bit about the different types of tranlogoptions settings? How can they be used to control what the extract process does? Nick: So one of the first ones is ExcludeTag. So GoldenGate has the ability to exclude tagged transactions. Within the database itself, you can actually specify a transaction to be tagged using a DBMS set tag option. GoldenGate replicat also sets its transactions with a tag so that the GoldenGate process knows which transactions were done by the replicat and it can exclude them automatically. You can do exclude tag with a plus. That simply means to exclude any transaction that's been tagged with any value. You can also exclude specific tags. Another good option for TranLogOptions is enable procedural replication. This allows GoldenGate to actually capture and replicate database procedure calls, and this would be things like DBMS AQ, NQ operations, or DQ operations. So if you're using Oracle advanced queuing and you need GoldenGate to replicate those changes, it can. Another valuable tranlogoption setting is enable auto capture. Within the Oracle Database, you can actually set ALTER TABLE command that says ALTER TABLE, enable logical replication. Or when you create a table, you can actually do CREATE TABLE statement and at the end use the enable logical replication option for that CREATE TABLE statement. And this tells GoldenGate to automatically capture that table. One of the nice features about this is that I don't need to specify that table and my parameter file, and it'll automatically enable supplemental logging on that table for me using scheduling columns. So it makes it very easy to set up replication between Oracle databases. 06:01 Nikita: Can you tell us about replicat parameters, Nick? Nick: Within a replicat, we'll have the group name, some common other parameters that we'll use is a mapping parameter that allows us to map the source to target table relationships. We can do transformation within the replicat, as well as error handling and controlling group operations to improve performance. Some common replicat parameters include the replicat parameter itself, which tells us what the name of that replicat is. We have our map statement, which allows us to map a source object to a target object. We have things like rep error that control how to handle errors. Insert all records allows us to change and convert, update, and delete operations into inserts. We can do things like compare calls, which helps with active-active replication in determining which columns are used in the GoldenGate WHERE clause. We also have the ability to use macros and column mapping to do additional transformation and make the parameter file look elegant. 07:07 AI is being used in nearly every industry…healthcare, manufacturing, retail, customer service, transportation, agriculture, you name it! And it’s only going to get more prevalent and transformational in the future. It’s no wonder that AI skills are the most sought-after by employers. If you’re ready to dive in to AI, check out the OCI AI Foundations training and certification that’s available for free! It’s the perfect starting point to build your AI knowledge. So, get going! Head on over to mylearn.oracle.com to find out more. 07:47 Nikita: Welcome back! Let’s move on to some of the most interesting topics within GoldenGate… data mapping, selection, and transformation. As I understand, users can do pretty cool things with GoldenGate. So Nick, let’s start with how GoldenGate can manipulate, change, and map data between two different databases. Nick: The map statement within a Replicat parameter allows you to provide specifications on how you're going to map source and target objects. You can also use a map and an extract, but it's pretty rare. And that would be used if you needed to write the object name. Inside the trail files is a different name than the actual object name that you're capturing from. GoldenGate can also do different data selection, mapping, and manipulation, and this is all controlled within the Extract and Replicat parameter files. In the classic architecture of GoldenGate, you could do a rudimentary level of transformation and filtering within the extract pump. Now, the distribution service is only allowing you to do filtering. Any transformation that you had within the pump would need to be moved to the Extract or the Replicat process. The other thing that you can do within GoldenGate is select and filter data based on different levels and conditions. So within your parameter clause, you have your Table and Map statement. That's the core of everything. You have your filtering. You have COLS and COLSEXCEPT, which allow you to determine which columns you're going to include or exclude from replication. The Table and Map statement works at the table level. The FILTER works at the row level. And COLS and COLSEXCEPTs works at the column level. We also have the ability to filter by operation type too. So GoldenGate has some very easy parameters called GitInserts, GitUpdates, GitDeletes, and conversely ignore updates, ignore deletes, ignore inserts. And that will affect the operation type. 09:40 Lois: Nick, are there any features that GoldenGate provides to make data replication easier? Nick: The first thing is that GoldenGate is going to automatically match your source and target column names with a parameter called USEDEFAULTS. You can specify it inside of your COLMAP clause, but again, it's a default, so you don't need to worry about it. We also handle all data type and character set conversion. Because we store the metadata in the trail, we know what that source data type is like. When we go to apply the record to the target table, the Replicat process is going to look up the definition of that record and keep a repository of that in memory. So that when it knows that, hey, this value coming in from the trail file is going to be of a date data type, and then this value in the target database is going to be a character data type, it knows how to convert that date to a character, and it'll do it for you. Most of the conversion is going to be done automatically for data types. Things where we don't do automatic data type conversion is if you're using abstract data types or user-defined data types, collections arrays, and then some types of CLOB operations. For example, if you're going from a BLOB to a JSON, that's not really going to work very well. Character set conversion is also done automatically. It's not necessarily done directly by GoldenGate, but it's done by the database engine. So there is a character set value inside that source database. And when GoldenGate goes to apply those changes into the target system, it's ensuring that that character set is visible and named so that that database can do the necessary translation. You can also do advanced filtering transformation. There's tokens that you can attach from the source environment, database, or records into a record itself on the trail file. And then there's also a bunch of metadata that GoldenGate can use to attach to the record itself. And then of course, you can use data transformation within your COLMAP statement. 11:28 Nikita: Before we wrap up, what types of data transformations can we perform, Nick? Nick: So there's quite a few different data transformations. We can do constructive or destructive transformation, aesthetic, and structural. 11:39 Lois: That’s it for the Oracle GoldenGate 23ai: Fundamentals series. I think we covered a lot of ground this season. Thank you, Nick, for taking us through it all. Nikita: Yeah, thank you so much, Nick. And if you want to learn more, head over to mylearn.oracle.com and search for the Oracle GoldenGate 23ai: Fundamentals course. Until next time, this is Nikita Abraham… Lois: And Lois Houston, signing off! 12:04 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
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Oracle GoldenGate 23ai: Managing Extract Trails and Files
06/24/2025
Oracle GoldenGate 23ai: Managing Extract Trails and Files
In this episode of the Oracle University Podcast, Lois Houston and Nikita Abraham explore the intricacies of trail files in Oracle GoldenGate 23ai with Nick Wagner, Senior Director of Product Management. They delve into how trail files store committed operations, preserving the order of transactions and capturing essential metadata. Nick explains that trail files are self-describing, containing database and table definition records, making them easier to work with. The episode also covers trail file management, including the purge trail task and the ability to download trail files directly from the web UI, providing flexibility in various deployment scenarios. Oracle GoldenGate 23ai: Fundamentals: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------------------------- Podcast Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Nikita: Welcome back to another episode of the Oracle University Podcast! I’m Nikita Abraham, Team Lead of Editorial Services with Oracle University, and I’m joined by Lois Houston, Director of Innovation Programs. Lois: Hi there! In our last episode, we discussed the Replicat process. That was a good introduction, and you should give it a listen if you’re interested in the fundamentals of GoldenGate 23ai. 00:49 Nikita: Nick Wagner, Senior Director of Product Management for Oracle GoldenGate, is back with us today to talk about how to manage Extract Trails and Files. Hi Nick, it’s a pleasure to have you with us. So, we’ve spoken about trail files in our earlier episodes. But can you tell us about the kind of information that’s actually stored in these files? Nick: The trail files contain committed operations only. In an Oracle environment, the extract process is actually able to understand and read both committed and uncommitted transactions. It holds the uncommitted activity and the cache manager associated settings. As a transaction is committed, it's then flushing that information to the trail file. All this information in the transaction is preserved, so we have not only the transaction itself, but the order of the operations within that transaction. All the changed columns, including the primary key and any scheduling columns are also captured, and this is controlled by the log or sub calls parameter and other parameters within the extract process. The data captured depends on settings in the extract file and you can include additional information, including tokens. The trail files also contain metadata information, where the trail files are what we call self-describing, which means that as we start reading in new objects, we start writing the definition of those objects into the trail file themselves. 02:11 Lois: Nick, what does the structure of a trail file look like? Nick: The trail files have a header information, which simply keeps information about what version of trail file this is, where GoldenGate is handling it, information about that trail file itself. You'll also have three different types of records. You'll have a data record, which contains the actual before and after images, the table update statement, the type of operations. You have a database definition record, which includes information about the database that GoldenGate is capturing from, and then you'll also have a table definition record. As GoldenGate starts up and creates a trail file for the first time, it's always going to write the trail file header and associated database definition record, and then it's going to start reading data out of the source database. As it encounters a new table for the first time in that trail file, it's going to write the metadata for that object as well. This makes it very easy. This means that within a single trail file, any data records I have in there, that trail file also contains the associated table definition record for that table. 03:20 Nikita: Let’s talk about compatibility between different versions of GoldenGate. How do the trail files fit into that? Nick: The GoldenGate trail files themselves have information built into them to help understand what they're compatible with as far as GoldenGate releases. If I'm replicating from a new version of GoldenGate to an older version of GoldenGate, I can set the format release value to tell the extract process to write these trail files in an older version. In this case, I can simply say format release 19 and it'll write the trail files in the 19C version. If you're going from an older version to a newer version of GoldenGate, it's automatically able to process the old version trail file structure without having to change anything. 04:02 Nikita: Now, GoldenGate is constantly generating these trail files as it runs. So, how do we manage them over time. What’s the cleanup process like? Nick: Within the GoldenGate microservices architecture, the web UI has a way to manage your trail files and clean them up. So there's a purge trail task that allows you to go in and set up rules on how long to keep the trail files around for before they're purged. We have customers that want to reposition extract and so you'll want to make sure that you keep trail files around long enough so that you can handle any reposition that you intend to do. Trail files will always be kept around even past their purge rules if they're still needed for GoldenGate recovery. Also new to GoldenGate 23ai is the ability to download trail files directly from the web UI. This is extremely helpful if you're using OCI GoldenGate or you don't have OS access on the machine where GoldenGate is running. 04:56 Lois: What if we want to look inside these trail files and actually see the individual records? How can we examine them directly? Nick: Well, that can be seen using a tool called Logdump. Logdumps utility, that's installed in your ogghome/bindirectory. It has online help as well as full documentation. 05:14 Lois: And how do you use Logdump? Nick: So to use Logdump, the first thing you'll do is launch the service and then you'll open a trail file. You would specify the full path of the trail file along with the path name and the sequence number of that trail file. Once you've set it up, you'll position into that location within that trail file. Normally people position at record 0 and then they'll do a next, which allows them to get the next information. There's a couple other commands in there, such as POS, which allows you to set the position, scan for header, allows you to scan to the next record if you position within the middle of a record. So, when you first run Logdump, it's not going to have very much information available for you. So, you'll want to turn on a couple of settings. You'll want to enable File Header, GHDR, and Detail to be able to see more information about what's going on within that record within the trail. Logdump also has the ability to show you the actual ASCII values as opposed to the text value. This is very useful for dealing with multibyte data as well as unprintable characters. You can also specify the length of the record to show for each Logdump record. And this is in the reclen parameter, 280 is a rough number and it will usually show about enough that'll fit on a single page. 06:40 Join the Oracle University Learning Community and tap into a vibrant network of over 1 million members, including Oracle experts and fellow learners. This dynamic community is the perfect place to grow your skills, connect with likeminded learners, and celebrate your successes. As a MyLearn subscriber, you have access to engage with your fellow learners and participate in activities in the community. Visit community.oracle.com/ou to check things out today! 07:12 Nikita: Welcome back! Nick, earlier you mentioned data records in trail files. What kind of information do these records contain? Nick: When we start looking at data records within the trail file, we're going to see a little bit different format. It's going to give us information about what type of operation this was, the before, after indicator, is this an after image or a before image? It's going to give us the time information. It's going to tell us what table this record was on and the values within that record. We can also count the number of records in a trail using the count option that tells us how many records in the trail, the average size, and then the operation type breakdown. We can also get some additional details on that count, including having it broken out by table and operation within those tables. This is really useful if you're trying to track down a missing record or an out of sync condition and you want to make sure that GoldenGate is appropriately capturing all the changes. We can also use an option within Logdump called scan for metadata. The shorthand for this command is sfmd, it allows you to scan for something like a database definition record. You may have multiple database definition records versions within the same trail file. It tells us what type of database this was, the character set, which is important because this information is used by the replica when it goes to apply changes into the target database. We can also scan for metadata to get table definition records. The data types are numeric values that are associated with an internal GoldenGate data type. 08:43 Lois: Thank you, Nick, for your insights. There’s a lot more you can find in the Oracle GoldenGate 23ai: Fundamentals course on MyLearn. So, make sure you check that out by visiting mylearn.oracle.com. Nikita: Join us next week for a discussion on parameters, data selection, filtering, and transformation. Until then, this is Nikita Abraham… Lois: And Lois Houston signing off! 09:07 That’s all for this episode of the Oracle University Podcast. 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Oracle GoldenGate 23ai: The Replicat Process
06/17/2025
Oracle GoldenGate 23ai: The Replicat Process
In this episode, Lois Houston and Nikita Abraham, along with Nick Wagner, Senior Director of Product Management, dive into the Replicat process in Oracle GoldenGate 23ai. They discuss how Replicat applies changes to the target database, highlighting the different types: Classic, Coordinated, and Parallel Replicat. Oracle GoldenGate 23ai: Fundamentals: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ---------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Hello and welcome to another episode of the Oracle University Podcast. I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hi everyone! If you’ve been listening to us these last few weeks, you’ll know we’ve been discussing the fundamentals of GoldenGate 23ai. Today is going to be all about the Replicat process. Again, this is something we’ve discussed briefly in earlier episodes, but just to recap, the Replicat process applies changes from the source database to the target database. It's responsible for reading trail files and applying the changes to the target system. 01:04 Lois: That’s right, Niki. And we’ll be chatting with Nick Wagner, Senior Director of Product Management for Oracle GoldenGate. Hi Nick! Thanks for joining us again today. Let’s get straight into it. Can you give us an overview of the Replicat process? Nick: One thing that's very important is the Replicat is extremely chatty with that target database. So it's going to be going in and trying to make lots of little transactions on that system. The Replicat process only issues single row DML. So if you can imagine a source database that's generating hundreds of thousands of changes per second, we're going to have to have a Replicat process that can do 100,000 changes per second on that target site. That means that it's going to have to send a lot of little one record commands. And so we've got a lot of ways to optimize that. But in all situations you're really going to want very, very low ping time between that Replicat process and that target database. This often means that if you're going to be running GoldenGate in a cloud, you're going to want the Cloud GoldenGate environment to be running in that target data center, wherever that target database is. 02:06 Lois: What are the key characteristics of the process, Nick? Nick: Replicat process is going to read the changes from the trail file and then apply them to the target system, just like any database user would. It's not doing anything special where it's going under the covers and trying to apply directly to the database blocks. It's just applying regular standard insert, update, delete, and DDL statements to that target database. A single trail file does support high volume of data replication activity depending on the type of Replicat. Replicats do preserve the boundary of their transactions. So in the situations, by default, a transaction that's on the source, let's say five inserts followed by a commit will remain five inserts followed by a commit on the target site. There are some operations and changes that do affect this, but they're not turned on by default. There are things like group transactions that allows you to group multiple transactions into a single commit. This one could actually improve performance in some cases. We also have batch SQL that can change the boundaries of a transaction as well. And then in a Parallel Replicat, you actually have the ability to split a large transaction into multiple chunks and apply those chunks in Parallel. So again, by default, it's going to preserve the boundaries, but there are ways to change that. And then the Replicats use a checkpoint table to help with recovery and to know where they're applying data and what they've done. The other thing in here is, like an Extract process can write to multiple trails and write subsets of data to each one, a Replicat can only process a single set of trail files at once. So it's going to be attached to a specific trail file like trail file AB, and will only be able to read changes from trail file AB. If I have multiple trails that need to be applied into a target system, then I have to set up multiple Replicats to handle that. 03:54 Nikita: So, what are the different Replicat types, Nick? Nick: We have three types in the product today. We have Classic Replicat, which should really only be used for testing purposes or in environments that don't support any of the other specialized Replicats. We have Coordinated Replicat, which is a high speed apply mechanism to apply data into a target system. It does have some parallelism in it, but it's user defined parallelism. And then we have our flagship and that's Parallel Replicat. And this is the most performant lowest latency Replicat that we have. 04:25 Lois: Ok. Let’s dive a little deeper into each of them, starting with the Classic Replicat. How does it work? Nick: It's pretty straightforward. You're going to have a process that reads the trail files, and then in a single threaded fashion it's going to take the trail file logical change record, convert it to an insert, update, or delete, and then apply it into that target database. Each transaction that it does is preceded by a change to the checkpoint table. So when the transaction that the Replicat is currently doing is committed, that checkpoint table update also gets committed. That way when the Replicat restarts, it knows exactly what transaction it left off and how it last applied the record. And all the Replicats work the same way with regards to checkpoint tables. They each have their own little method of ensuring that the transaction they're applying is also reflected within the checkpoint table so that when it restarts, it knows exactly where it happened. That way, if a Replicat dies in the middle of a transaction, it can be restarted without any duplicate data or without missing data. 05:29 Did you know that Oracle University offers free courses on Oracle Cloud Infrastructure? You’ll find training on everything from multicloud, database, networking, and security to artificial intelligence and machine learning, all free for our subscribers. So, what are you waiting for? Pick a topic, head over to mylearn.oracle.com, and get started. 05:53 Nikita: Welcome back! Moving on, what about Coordinated Replicat? Nick: The Coordinated Replicat is going to read from a set of trail files. It's going to have multiple threads that do this. So you have your base thread, your coordinated thread that's going to be thread 1. It's going to process the data and apply it into that target database. You then have thread 2, 4, 5, 6, and so on. When you set up your Replicat parameter file for a Coordinated Replicat, the map commands that maps from one table on the source to a table on the target has an additional option. So you'll have an option called a range or thread range. With the range and thread range option, you can actually tell which table to go into which thread. 06:38 Lois: Can you give us an example of this? Nick: So I could say map Scott.M into thread 1 and I want Scott.Dept into thread 2. Well, this is fantastic until you realize that Scott.M and Scott.Dept have a foreign key between them or a child dependencies, parent-child relationships. What that means is that now I'm going to have to disable that foreign key on the target site, because there's no way for GoldenGate to coordinate the changes in one thread to another thread. And so you really have to be careful on how you pair your tables together. If you don't have any referential integrity on that target database, then you can use parallel coordinated Replicat to really high degrees of parallelism, and you get some very good performance out of it. Let's say that you have a table that's really got too much data for even a single thread to process, that's where the thread range comes in. And thread range command will use something like the table's primary key to split transactions on that table across multiple threads. So I can say, hey, take my table Scott.M and I want to spread transactions across threads 10, 11, 12, 13, and 14 and then spread them evenly based on the primary key. And Coordinated Replicat will do that. So you can get some very high performance numbers out of it and you can really fine tune the tables, especially if you know the amount of data coming into each one. While this does work great, we observed that a lot of customers really don't know their applications to that level of detail, and so we needed a different method to push data into that target database, where we could define the parallelism based on the database expectations. So instead of the customer having to try and figure out what are the parent-child relationships, why can't GoldenGate do it for me? And that led to Parallel Replicat. 08:26 Nikita: And what are the benefits and features of the Parallel Replicat process? Nick: So Parallel Replicat has been around for quite a few years now. It supports most targets, it was Oracle initially, but now it's been expanded out to a lot of the non-Oracle targets and even some of the nonrelational database targets. It has absolutely the best performance of any Replicat process out there. You can use it to split large transactions as well. So if all of a sudden you have a batch job that does a million inserts followed by a single commit, I can split that across 10 threads, each thread doing 100,000 inserts. And it's aware of your transaction dependencies, that's the cool thing. So in Coordinated Replicat, you had to worry about how to split your tables up, in Parallel Replicat, we do it for you. 09:11 Lois: And how does Parallel Replicat work? Nick: So there's three main processes to the Parallel Replicat. You have your first is the mapper process. This is going to be responsible for taking the data out of the trail files and putting them into kind of our collator and scheduler box. As transactions go from the trail file, they get put into this box in memory where they're processed. There's a collator process that will look at these processes and go, OK, as they're coming in, let me read some of the data in them to determine how they can be applied in Parallel or not. And so the collator process understands the foreign key dependencies on that target database. And it's able to say, hey, I know that my two tables are these two tables, have a parent-child relationship, I need to make sure that changes on those tables go in the correct order. And so if all of a sudden you see an insert using the parent record and then another insert into the child record and they're mapped together, GoldenGate will ensure that those two transactions go serially and not parallel where they could get applied out of order. There's then a scheduler process that's going to look at this and say, OK, now that I'm taking transactions from the collator process, who's already identified whether or not transactions can be applied in parallel or serial, and I'm going to feed them off to applier processes that are ready and waiting for me to apply those changes into the database. And then the applier process is waiting for the scheduler process to send its transactions and say, OK, what's my next one? Where's the next transaction I should be working on and applying? And then the applier process is the one actually applying the changes into that target database, again, just using standard DML operations. So there's a lot of benefits to this one. You don't need to worry about your foreign key dependencies, you can leave all your foreign keys enabled. The collator process will actually use information within the trail file to determine which transactions can be applied in parallel, and which one needs to be applied serially. 11:13 Lois: Thank you, Nick, for this insightful conversation. There’s loads more to discover about the Replicat process, and you can do that by heading over to mylearn.oracle.com and searching for the Oracle GoldenGate 23ai: Fundamentals course. Nikita: In our next episode, Nick will take us through managing Extract Trails and Files. Until then, this is Nikita Abraham… Lois: And Lois Houston, signing off! 11:37 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
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Oracle GoldenGate: Distribution Path, Target Initiated Path, Receiver Server, and Initial Load
06/10/2025
Oracle GoldenGate: Distribution Path, Target Initiated Path, Receiver Server, and Initial Load
In this episode, Lois Houston and Nikita Abraham dive into key components of Oracle GoldenGate 23ai with expert insights from Nick Wagner, Senior Director of Product Management. They break down the Distribution Service, explaining how it moves trail files between environments, replaces the classic extract pump, and ensures secure data transfer. Nick also introduces Target Initiated Paths, a method for connecting less secure environments to more secure ones, and discusses how the Receiver Service simplifies monitoring and management. The episode wraps up with a look into Initial Load, covering different methods for syncing source and target databases without downtime. Oracle GoldenGate 23ai: Fundamentals: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ----------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Nikita: Welcome to the Oracle University Podcast! I’m Nikita Abraham, Team Lead of Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hey there! Last week, we spoke about the Extract process and today we’re going to spend time discussing the Distribution Path, Target Initiated Path, Receiver Server, and Initial Load. These are all critical components of the GoldenGate architecture, and understanding how they work together is essential for successful data replication. 00:58 Nikita: To help us navigate these topics, we’ve got Nick Wagner joining us again. Nick is a Senior Director of Product Management for Oracle GoldenGate. Hi Nick! Thanks for being with us today. To kick things off, can you tell us what the distribution service is and how it works? Nick: A distribution path is used when we need to send trail files between two different GoldenGate environments. The distribution service replaces the extract pump that was used in GoldenGate classic architecture. And so the distribution service will send the trail files as they're being created to that receiver service and it will write the trail files over on the target system. The distribution service works in a kind of a streaming fashion, so it's constantly pulling the trail files that the extract is creating to see if there's any new data. As soon as it sees new data, it'll packet it up and send it across the network to the receiver service. It can use a couple of different methods to do this. The most secure and recommended method is using a WebSocket secure connection or WSS. If you're going between a microservices and a classic architecture, you can actually tell the distribution service to send it using the classic architecture method. In that case, it's the OGG option when you're configuring the distribution service. There's also some unsecured methods that would send the trail files in plain text. The receiver service is then responsible for taking that data and rewriting it into the trail file on the target site. 02:23 Lois: Nick, what are some of the key features and responsibilities of the distribution service? Nick: It's responsible for command deployment. So any time that you're going to actually make a command to the distribution service, it gets handled there directly. It can handle multiple commands concurrently. It's going to dispatch trail files to one or more receiver servers so you can actually have a single distribution path, send trail files to multiple targets. It can provide some lightweight filtering so you can decide which tables get sent to the target system. And it also is integrated in with our data streams, our pub and subscribe model that we've added in GoldenGate 23ai. 03:01 Lois: Interesting. And are there any protocols to remember when using the distribution service? Nick: We always recommend a secure WebSocket. You also have proxy support for use within cloud environments. And then if you're going to a classic architecture GoldenGate, you would use the Oracle GoldenGate protocol. So in order to communicate with the distribution service and send it commands, you can communicate directly from any web browser, client software-- installation is not required-- or you can also do it through the admin client if necessary, but you can do it directly through browsers. 03:33 Nikita: Ok, let's move on to the target initiated path. Nick, what is it and what does it do essentially? Nick: This is used when you're communicating from a less secure environment to a more secure environment. Often, this requires going through some sort of DMZ. In these situations, a connection cannot be established from the less secure environment into the more secure environment. It actually needs to be established from the more secure environment out. And so if we need to replicate data into a more secure environment, we need to actually have the target GoldenGate environment initiate that connection so that it can be established. And that's what a target-initiated path does. 04:12 Lois: And how do you set it up? Nick: It's pretty straightforward to set up. You actually don't even need to worry about it on the source side. You actually set it up and configure it from the target. The receiver service is responsible for receiving the trail file data and writing it to the local trail file. In this situation, we have a target-initiated path created. And so that receiver service is going to write the trail files locally and the replicat is going to apply that data into that target system. 04:37 Nikita: I also want to ask you about the Receiver service. What is it really? Nick: Receiver service is pretty straightforward. It's a centrally controlled service. It allows you to view the status of your distribution path and replaces target side collectors that were available in the classic architecture of GoldenGate. You can also get statistics about the receiver service directly from the web UI. You can get detailed information about these paths by going into the receiver service and identifying information like network details, transfer protocols, how many bytes it's received, how many bytes it's sent out. If you need to issue commands from the admin client to the receiver service, you can use the info command to get details about it. Info all will tell you everything that's running. And you can see that your receiver service is up and running. 05:28 Are you working towards an Oracle Certification this year? Join us at one of our certification prep live events in the Oracle University Learning Community. Get insider tips from seasoned experts and learn from others who have already taken their certifications. Go to community.oracle.com/ou to jump-start your journey towards certification today! 05:53 Nikita: Welcome back. In the last section of today’s episode, we’ll cover what Initial Load is. Nick, can you break down the basics for us? Nick: So, the initial load is really used when you need to synchronize the source and target systems. Because GoldenGate is designed for 24/7 environments, we need to be able to do that initial load without taking downtime on the source. And so all the methods that we talk about do not require any downtime for that source database. 06:18 Lois: How do you do the initial load? Nick: So there's a couple of different ways to do the initial load. And it really depends on what your topology is. If I'm doing like-to-like replication in a homogeneous environment, we'll say Oracle-to-Oracle, the best options are to use something that's integrated with GoldenGate, some sort of precise instantiation method that does not require HandleCollisions. That's something like a database backup and restoring it to a specific SDN or CSN value using a Database Snapshot. Or in some cases, we can use Oracle Data Pump integration with GoldenGate. There are some less precise instantiation options, which do require HandleCollisions. We also have dissimilar initial load methods. And this is typically when you're going between heterogeneous environments. When my source and target databases don't match and there isn't any kind of fast unload or fast load utility that I could use between those two databases. In almost all cases, this does require HandleCollisions to be used. 07:16 Nikita: Got it. So, with so many options available, are there any advantages to using GoldenGate's own initial load method? Nick: While some databases do have very good fast load and unload utilities, there are some advantages to using GoldenGate's own initial load method. One, it supports heterogeneous replication environments. So if I'm going from Postgres to Oracle, it'll do all the data type transformation, character set transformation for me. It doesn't require any downtime, if certain conditions are met. It actually performs transformation as the data is loaded, too, as well as filtering. And so any transformation that you would be doing in your normal transaction log replication or CDC replication can also go through the same transformation for the initial load process. GoldenGate's initial load process does read directly from the source tables. And it fetches the data in arrays. It also uses parallel processing to speed up the replication. It does also handle activity on the source tables during the initial load process, so you do not need to worry about quiescing that source database. And a lot of the initial load methods directly built into GoldenGate support distributed application analytics targets, including things like Databricks, Snowflake, BigQuery. 08:28 Lois: And what about its limitations? Or to put it differently, when should users consider using different methods? Nick: So the first thing to consider is system proximity. We want to make sure that the two systems we're working with are close together. Or if not, how are we going to send the data across? One thing to keep in mind, when we do the initial load, the source database is not quiesced. So if it takes an hour to do the initial load or 10 hours, it really doesn't matter to GoldenGate. So that's something to keep in mind. Even though we talk about performance of this, the performance really isn't as critical as one might suspect. So the important thing about data system proximity is the proximity to the extract and replicat processes that are going to be pulling the data out and pushing it across. And then how much data is generated? Are we talking about a database that's just a couple of gigabytes? Or are we talking about a database that's hundreds of terabytes? Do we want to consider outage time? Would it be faster to take a little bit of outage and use some other method to move the data across? What kind of outage or downtime windows do we have for these environments? And then another consideration is disk space. As we're pulling the data out of that source database, we need to have somewhere to store it. And if we don't have enough disk space, we need to run to temporary space or to use multiple external drives to be able to support it. So these are all different considerations. 09:50 Nikita: I think we can wind up our episode with that. Thanks, Nick, for giving us your insights. Lois: If you'd like to learn more about the topics we covered today, head over to mylearn.oracle.com and check out the Oracle GoldenGate 23ai: Fundamentals course. Nikita: In our next episode, Nick will take us through the Replicat process. Until then, this is Nikita Abraham… Lois: And, Lois Houston signing off! 10:14 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. 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Oracle GoldenGate 23ai: The Extract Process
06/03/2025
Oracle GoldenGate 23ai: The Extract Process
The Extract process is the heart of Oracle GoldenGate 23ai, capturing data changes with precision. In this episode, Lois Houston and Nikita Abraham sit down with Nick Wagner, Senior Director of Product Management, to break down Extract’s role, architecture, and best practices. Learn how Extract works across different setups, from running on source databases to using a Hub model for greater flexibility. Additionally, understand how trail files, parameter files, and naming conventions impact performance. Oracle GoldenGate 23ai: Fundamentals: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. -------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me today is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hi everyone! Last week, we spoke about installing GoldenGate and today, we’re diving into the Extract process. We’ve discussed it briefly in an earlier episode, but to recap, the Extract process captures changes from the source database and writes them to a trail file. 00:54 Lois: Joining us again is Nick Wagner, Senior Director of Product Management for Oracle GoldenGate. Hi Nick! Before we get into the Extract process, can you walk us through the different architecture options available for GoldenGate. Let’s start with when GoldenGate is installed on the same server as the source database. What are the benefits of this architecture? Nick: There's a couple of advantages to this. It means that GoldenGate can use the same resources on that source database. It means that you don't need another host to support the GoldenGate environment. It also means that GoldenGate can use a bequeathed connection to connect from the Extract process into the source database to make it run faster. The restrictions on this are that the Replicat process is highly communicative with the target database. What that really means is that the Replicat process is constantly doing lots of little transactions. And so the network latency between the Replicat process and the target database should really be around 4 milliseconds or less for optimal performance. So that means that a lot of people can't really run GoldenGate on the source system, even though it's an option, because they need that Replicat latency performance. And so they'll often install GoldenGate on the same server as the target database. In this case, they can use the Replicat to connect using a bequeath connection to that target system, you know that it's going to be highly performant and that latency is not going to be an issue. This works really well because the Extract process has actually been optimized to do remote capture. And so it's actually able to handle 80 milliseconds round trip ping time or less between the actual Extract process and the source database itself. And so a lot of customers will opt for this method, where they're actually running GoldenGate away from the target, or excuse me, away from the source database. 02:44 Nikita: Interesting. And is there an option where you don’t need to install GoldenGate on the actual source or target database? Nick: We also have another architecture pattern called a Hub model. And this is what you would see in something like OCI GoldenGate or OCI Marketplace, or even in third party clouds environments where you don't have the ability to install GoldenGate on the actual source or target database. In these cases, GoldenGate is just going to run on a virtual machine or an environment that you have set up specifically for GoldenGate. Now, this GoldenGate Hub doesn't need to have any database software installed. It doesn't need to have any database information on it. It's simply working as a client. So GoldenGate Extract process is a client connecting into the source database and the Replicat is a client connecting into the target database. And this really gives you a lot of flexibility. However, in some cases, there may be too much of a distance, so you won't be able to get both less than 80 milliseconds on the source side in less than 4 milliseconds on the round trip on the target side. And so in that case, you can have multiple GoldenGate Hubs. And so you would have a Hub on the Extract side and another Hub on the Replicat side. And all these are fully accessible. In this case, you'll actually use the distribution service to send the trail files from one system to another. 04:00 Lois: So, coming to the Extract process, what does it actually do? Nick: The Extract process is configured to capture changes from that source database. In different terminology, it can subscribe to a topic if we're pulling data out of a Kafka queue or a topic or some messaging system like a JMS queue and relational database language, we're pulling database from the database transaction logs. There's a lot of different sources and targets. You can always use the GoldenGate Certification Matrix to determine which sources and targets are supported, and where we can extract data from. The capture process also connects to the source table for initial loads. When we do the initial load, instead of reading from the transaction logs, GoldenGate is actually going to do a select star on that table to get the information it needs for that load. 04:49 Lois: And what about the Extract process group? Nick: The process group is kind of a grouping of the process itself, which is either going to be my Extract or Replicat and associated files. So in an Extract environment, we have our parameter file and a report file and our checkpoint files. The parameter file, the .prm file, is going to list out which objects we're going to capture and how we're going to capture that data. It also controls what we're going to be writing to the trail file and where that trail file exists. The report file is really just a log of what's going on in that Extract process, how it's working, what tables it's encountered. It's used for any troubleshooting to make sure everything is running smoothly. And then you also have the checkpoint files. The checkpoint files and report files should not be modified by the user, the parameter file can be. The checkpoint files are going to include information about where that process is reading from, where it's writing to, and any open transaction that it's tracking as part of the bounded recovery or cache manager functionality. 05:54 Nikita: How do you go about creating an Extract group? Nick: The Extract group can be created by doing an Add Extract command or through the UI. Each Extract must also have a unique name. On the Extract process side, there is an eight-character hard limit for the name itself. And so, you can’t have an Extract process called my Extract for today is called Nick. More than eight characters. 06:17 Lois: Nick, I was wondering, is there a simple way to identify what an Extract or Replicat is doing? Nick: If you need something to help identify what that Extract or Replicat is doing or the description of it, we do have a description field. So when you do the Add Extract or Add Replicat, there is a DESC field that allows you to add more details in. And this is really key because it allows you to put a lot more information that’s going to show up in all the log files at the service manager level. And any time you do an info on the service it’ll also bring up that description field so you can see what’s going on. That way, if you get an alert, a watch, you need to keep track of something you can easily identify what that process is doing and what it’s replicating for. 07:06 Adopting a multicloud strategy is a big step towards future-proofing your business and we’re here to help you navigate this complex landscape. With our suite of courses, you'll gain insights into network connectivity, security protocols, and the considerations of working across different cloud platforms. Start your journey to multicloud today by visiting mylearn.oracle.com. 07:32 Nikita: Welcome back! Before the break, we were talking about the description field, which helps identify what the Extract is doing. Nick, are there any best practices to keep in mind when naming a group? Nick: You also don't want to use any special characters when naming the group, especially you know things like slashes or dashes. You don't want to use spaces in them, just really stick to alphanumeric characters only. The group names are also case insensitive, so EDEPT, all capitalized is the same as edept lowercase. The other thing that you don't want to do and this isn't a hard restriction, it's just more of a friendly reminder is don't end your group with a numeric value. The report files themselves end in numeric values, so you'll have a report file, 0123456789, and so on. If you were to end your group name with a numeric value, then it can often be confused for a report file. And so you don't want to really do that. But otherwise you're free to call it whatever you want. 08:39 Lois: Got it. What about naming conventions? Are there any rules that apply? Nick: You can use whatever naming convention you want, but again, try and follow these best practices. No strange characters and don't end your process names with a numeric value. 08:53 Nikita: Can you explain the role of parameter and trail files in the Extract process? Nick: The parameter files is really where GoldenGate is doing all of its hard work. And there's two parameter files, there's the GLOBALS file that's configured at the service manager level, and then you have your actual process parameter files that are configured at your Extract, Replicat, distribution service levels. We also have our trail files. We've talked a little bit about the trail file as they're the continuous source of information for GoldenGate. They can exist on the source or target or intermediate system. An Extract process can write to multiple trail files, but a trail file can only be written to by one process at a time. A trail file also consists of a two-letter abbreviation, and then we have that nine-digit sequence number. And then the processes that read a trail file include the Distribution Path or Target Initiated Path, as well as the Replicat. And again, just as a quick reminder, all the trail files are stored in the ogg_var_home/libdata directory. This is important because they do grow pretty rapidly, and so if you need to keep track of the space used in that directory, this is an important thing to do. 10:05 Nikita: How does GoldenGate handle data extraction for different database systems? Nick: You have a couple of different options. There's for non-Oracle databases, you have a classic extract. And these read directly from the transaction logs themselves or they call an API within that target database. For example, within DB2, we actually use the DB2 API to pull data out of the transaction logs. In something like Postgres, we're getting data directly out of the transaction logs by reading it. And that's put in there by the test decoding plugin. With Oracle, it's a little bit different. Because it's actually integrated in with the database kernel itself, all the log mining is done inside the database engine. All GoldenGate is doing is connecting to that log mining area and getting the data from it. We also have the option of doing standby redo logs or downstream capture, which allows GoldenGate to read data from the standby redo logs. It's still an integrated extract. So GoldenGate isn't directly reading the standby redo logs, but it allows you to set it up in a different way. And then we have an initial load extract, which is to pull out the base data out of the tables by doing a select star on the tables. And this does not include any change data. So the initial loads and the what we call tran log extracts are separate. 11:24 Lois: Before we wrap up, can you quickly walk us through the key steps to configure an extract? Nick: So the first thing we want to do is set up a restart profile so that if the process fails, it'll automatically restart. Next thing we do is we create the extract parameter file. Then we'll go ahead and register the extract with the database. This is important in Oracle and also with a couple of other databases that we support where we actually need to tell the database engine that, hey, we're going to be setting up a process that's going to pull data out of that environment. Then we go ahead and add the extract process. We add the trail file so that it knows where to write to. And then we can go ahead and start the extract process and we'll be on our way to configuring replication. If necessary, we can configure a distribution service path as well. 12:09 Nikita: Thanks, Nick, for telling us about the Extract process. If you'd like to learn more about what we discussed today, head over to mylearn.oracle.com and check out the Oracle GoldenGate 23ai: Fundamentals course. Lois: You'll find detailed instructions to help you get started. In the next episode, we’ll go through the Distribution Path, Target Initiated Path, Receiver Server, and Initial Load. Until then, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 12:37 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
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Oracle GoldenGate Installation
05/27/2025
Oracle GoldenGate Installation
Installing Oracle GoldenGate 23ai is more than just running a setup file—it’s about preparing your system for efficient, reliable data replication. In this episode, Lois Houston and Nikita welcome back Nick Wagner to break down system requirements, storage considerations, and best practices for installing GoldenGate. You’ll learn how to optimize disk space, manage trail files, and configure network settings to ensure a smooth installation. Oracle GoldenGate 23ai: Fundamentals: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Nikita: Hello and welcome to Oracle University Podcast! I’m Nikita Abraham, Team Lead of Editorial Services with Oracle University, and I’m joined by Lois Houston, Director of Innovation Programs. Lois: Hi there! Last week, we took a close look at the security strategies of Oracle GoldenGate 23ai. In this episode, we’ll discuss all aspects of installing GoldenGate. 00:48 Nikita: That’s right, Lois. And back with us today is Nick Wagner, Senior Director of Product Management for GoldenGate at Oracle. Hi Nick! I’m going to get straight into it. What are the system requirements for a typical GoldenGate installation? Nick: As far as system requirements, we're going to split that into two sections. We've got an operating system requirements and a storage requirements. So with memory and disk, and I know that this isn't the answer you want, but the answer is that it varies. With GoldenGate, the amount of CPU usage that is required depends on the number of extracts and replicats. It also depends on the number of threads that you're going to be using for those replicats. Same thing with RAM and disk usage. That's going to vary on the transaction sizes and the number of long running transactions. 01:35 Lois: And how does the recovery process in GoldenGate impact system resources? Nick: You've got two things that help the extract recovery. You've got the bonded recovery that will store transactions over a certain length of time to disk. It also has a cache manager setting that determines what gets written to disk as part of open transactions. It's not just the simple answer as, oh, it needs this much space. GoldenGate also needs to store trail files for the data that it's moving across. So if there's network latency, or if you expect a certain network outage, or you have certain SLAs for the target database that may not be met, you need to make sure that GoldenGate has enough room to store its trail files as it's writing them. The good news about all this is that you can track it. You can use parameters to set them. And we do have some metrics that we'll provide to you on how to size these environments. So a couple of things on the disk usage. The actual installation of GoldenGate is about 1 to 1.5 gig in size, depending on which version of GoldenGate you're going to be using and what database. The trail files themselves, they default to 500 megabytes apiece. A lot of customers keep them on disk longer than they're necessary, and so there's all sorts of purging options available in GoldenGate. But you can set up purge rules to say, hey, I want to get rid of my trail files as soon as they're not needed anymore. But you can also say, you know what? I want to keep my trail files around for x number of days, even if they're not needed. That way they can be rebuilt. I can restore from any previous point in time. 03:15 Nikita: Let’s talk a bit more about trail files. How do these files grow and what settings can users adjust to manage their storage efficiently? Nick: The trail files grow at about 30% to 35% of the generated redo log data. So if I'm generating 100 gigabytes of redo an hour, then you can expect the trail files to be anywhere from 30 to 35 gigabytes an hour of generated data. And this is if you're replicating everything. Again, GoldenGate's got so many different options. There's so many different ways to use it. In some cases, if you're going to a distributed applications and analytics environment, like a Databricks or a Snowflake, you might want to write more information to the trail file than what's necessary. Maybe I want additional information, such as when this change happened, who the user was that made that change. I can add specific token data. You can also tell GoldenGate to log additional records or additional columns to the trail file that may not have been changed. So I can always say, hey, GoldenGate, replicate and store the entire before and after image of every single row change to your trail file, even if those columns didn't change. And so there's a lot of different ways to do it there. But generally speaking, the default settings, you're looking at about 30% to 35% of the generated redo log value. System swap can fill up quickly. You do want this as a dedicated disk as well. System swap is often used for just handling of the changes, as GoldenGate flushes data from memory down to disk. These are controlled by a couple of parameters. So because GoldenGate is only writing committed activity to the trail file, the log reader inside the database is actually giving GoldenGate not only committed activity but uncommitted activity, too. And this is so it can stay very high speed and very low latency. 05:17 Lois: So, what are the parameters? Nick: There's a cache manager overall feature, and there's a cache directory. That directory controls where that data is actually stored, so you can specify the location of the uncommitted transactions. You can also specify the cache size. And there's not only memory settings here, but there's also disk settings. So you can say, hey, once a cache size exceeds a certain memory usage, then start flushing to disk, which is going to be slower. This is for systems that maybe have less memory but more high-speed disk. You can optimize these parameters as necessary. 05:53 Nikita: And how does GoldenGate adjust these parameters? Nick: For most environments, you're just going to leave them alone. They're automatically configured to look at the system memory available on that system and not use it all. And then as soon as necessary, it'll overflow to disk. There's also intelligent settings built within these parameters and within the cache manager itself that if it starts seeing a lull in activity or your traditional OLTP type responses to actually free up the memory that it has allocated. Or if it starts seeing more activity around data warehousing type things where you're doing large transactions, it'll actually hold on to memory a little bit longer. So it kinda learns as it goes through your environment and starts replicating data. 06:37 Lois: Is there anything else you think we should talk about before we move on to installing GoldenGate? Nick: There's a couple additional things you need to think of with the network as well. So when you're deploying GoldenGate, you definitely want it to use the fastest network. GoldenGate can also use a reverse proxy, especially important with microservices. Reverse proxy, typically we recommend Nginx. And it allows you to access any of the GoldenGate microservices using a single port. GoldenGate also needs either host names or IP addresses to do its communication and to ensure the system is available. It does a lot of communication through TCP and IP as well as WSS. And then it also handles firewalls. So you want to make sure that the firewalls are open for ingress and egress for GoldenGate, too. There's a couple of different privileges that GoldenGate needs when you go to install it. You'll want to make sure that GoldenGate has the ability to write to the home where you're installing it. That's kind of obvious, but we need to say it anyways. There's a utility called oggca.sh. That's the GoldenGate Configuration Assistant that allows you to set up your first deployments and manage additional deployments. That needs permissions to write to the directories where you're going to be creating the new deployments. The extract process needs connection and permissions to read the transaction logs or backups. This is not important for Oracle, but for non-Oracle it is. And then we also recommend a dedicated database user for the extract and replicat connections. 08:15 Are you keen to stay ahead in today's fast-paced world? We’ve got your back! Each quarter, Oracle rolls out game-changing updates to its Fusion Cloud Applications. And to make sure you’re always in the know, we offer New Features courses that give you an insider’s look at all of the latest advancements. Don't miss out! Head over to mylearn.oracle.com to get started. 08:41 Nikita: Welcome back! So Nick, how do we get started with the installation? Nick: So when we go to the install, the first thing you're going to do is go ahead and go to Oracle's website and download the software. Because of the way that GoldenGate works, there's only a couple moving parts. You saw the microservices. There's five or six of them. You have your extract, your replicat, your distribution service, trail files. There's not a lot of moving components. So if something does go wrong, usually it affects multiple customers. And so it's very important that when you go to install GoldenGate, you're using the most recent bundle patch. And you can find this within My Oracle Support. It's not always available directly from OTN or from the Oracle e-delivery website. You can still get them there, but we really want people going to My Oracle Support to download the most recent version. There's a couple of environment variables and certificates that you'll set up as well. And then you'll run the Configuration Assistant to create your deployments. 09:44 Lois: Thanks, Nick, for taking us though the installation of GoldenGate. Because these are highly UI-driven topics, we recommend that you take a deep dive into the GoldenGate 23ai Fundamentals course, available on mylearn.oracle.com. Nikita: In our next episode, we’ll talk about the Extract process. Until then, this is Nikita Abraham… Lois: And Lois Houston signing off! 10:08 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
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Oracle GoldenGate 23ai Security Strategies
05/20/2025
Oracle GoldenGate 23ai Security Strategies
GoldenGate 23ai takes security seriously, and this episode unpacks everything you need to know. GoldenGate expert Nick Wagner breaks down how authentication, access roles, and encryption protect your data. Learn how GoldenGate integrates with identity providers, secures communication, and keeps passwords out of storage. Understand how trail files work, why they only store committed data, and how recovery processes prevent data loss. Whether you manage replication or just want to tighten security, this episode gives you the details to lock things down without slowing operations. Oracle GoldenGate 23ai: Fundamentals: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. -------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Welcome, everyone! This is our fourth episode on Oracle GoldenGate 23ai. Last week, we discussed the terminology, different processes and what they do, and the architecture of the product at a high level. Today, we have Nick Wagner back with us to talk about the security strategies of GoldenGate. 00:56 Lois: As you know by now, Nick is a Senior Director of Product Management for GoldenGate at Oracle. He’s played a key role as one of the product designers behind the latest version of GoldenGate. Hi Nick! Thank you for joining us again. Can you tell us how GoldenGate takes care of data security? Nick: So GoldenGate authentication and authorization is done in a couple of different ways. First, we have user credentials for GoldenGate for not only the source and target databases, but also for GoldenGate itself. We have integration with third-party identity management products, and everything that GoldenGate does can be secured. 01:32 Nikita: And we must have some access roles, right? Nick: There's four roles built into the GoldenGate product. You have your security role, administrator, operator, and user. They're all hierarchical. The most important one is the security user. This user is going to be the one that provides the administrative tasks. This user is able to actually create additional users and assign roles within the product. So do not lose this password and this user is extremely important. You probably don't want to use this security user as your everyday user. That would be your administrator. The administrator role is able to perform all administrative tasks within GoldenGate. So not only can they go in and create new extracts, create new replicats, create new distribution services, but they can also start and stop them. And that's where the operator role is and the user role. So the operator role allows you to go in and start/stop processes, but you can't create any new ones, which is kind of important. So this user would be the one that could go in and suspend activity. They could restart activity. But they can't actually add objects to replication. The user role is really a read-only role. They can come in. They can see what's going on. They can look at the log files. They can look at the alerts. They can look at all the watches and see exactly what GoldenGate is doing. But they're unable to make any changes to the product itself. 02:54 Lois: You mentioned the roles are hierarchical in nature. What does that mean? Nick: So anything that the user role does can be done by the operator. Anything that the operator and user roles can do can be done by the administrator. And anything that the user, operator, and administrator roles do can be done by the security role. 03:11 Lois: Ok. So, is there a single sign-on available for GoldenGate? Nick: We also have a password plugin for GoldenGate Connections. A lot of customers have asked for integration with whatever their single sign-on utility is, and so GoldenGate now has that with GoldenGate 23ai. So these are customer-created entities. So, we have some examples that you can use in our documentation on how to set up an identity provider or a third-party identity provider with GoldenGate. And this allows you to ensure that your corporate standards are met. As we started looking into this, as we started designing it, every single customer wanted something different. And so instead of trying to meet the needs for every customer and every possible combination of security credentials, we want you to be able to design it the way you need it. The passwords are never stored. They're only retrieved from the identity provider by the plugin itself. 04:05 Nikita: That’s a pretty important security aspect…that when it’s time to authenticate a user, we go to the identity provider. Nick: We're going to connect in and see if that password is matching. And only then do we use it. And as soon as we detect that it's matched, that password is removed. And then for the extract and replicats themselves, you can also use it for the database, data source, and data target connections, as well as for the GoldenGate users. So, it is a full-featured plugin. So, our identity provider plugin works with IAM as well as OAM. These are your standard identity manager authentication methods. The standard one is OAuth 2, as well as OIDC. And any Identity Manager that uses that is able to integrate with GoldenGate. 04:52 Lois: And how does this work? Nick: The way that it works is pretty straightforward. Once the user logs into the database, we're going to hand off authentication to the identity provider. Once the identity provider has validated that user's identity and their credentials, then it comes back to GoldenGate and says that user is able to log in to either GoldenGate or the application or the database. Once the user is logged in, we get that confirmation that's been sent out and they can continue working through GoldenGate. So, it's very straightforward on how it works. There's also a nice little UI that will help set up each additional user within those systems. All the communication is also secured as well. So any communication done through any of the GoldenGate services is encrypted using HTTPS. All the REST calls themselves are all done using HTTPS as well. All the data protection calls and all the communication across the network when we send data across a distribution service is encrypted using a secure WebSocket. And there's also trail file encryption at the operating system level for data at REST. So, this really gives you the full level of encryption for customers that need that high-end security. GoldenGate does have an option for FIPS 140-2 compliance as well. So that's even a further step for most of those customers. 06:12 Nikita: That’s impressive! Because we want to maintain the highest security standards, right? Especially when dealing with sensitive information. I now want to move on to trail files. In our last episode, we briefly spoke about how they serve as logs that record and track changes made to data. But what more can you tell us about them, Nick? Nick: There's two different processes that write to the trail files. The extract process will write to the trail file and the receiver service will write to the trail file. The extract process is going to write to the trail file as it's pulling data out of that source database. Now, the extract process is controlled by a parameter file, that says, hey, here's the exact changes that I'm going to be pulling out. Here's the tables. Here's the rows that I want. As it's pulling that data out and writing it to the trail files, it's ensuring that those trail files have enough information so that the replicat process can actually construct a SQL statement and apply that change to that target platform. And so there's a lot of ways to change what's actually stored in those trail files and how it's handled. The trail files can also be used for initial loads. So when we do the initial load through GoldenGate, we can grab and write out the data for those tables, and that excludes the change data. So initial loads is pulling the data directly from the tables themselves, whereas ongoing replication is pulling it from the transaction logs. 07:38 Lois: But do we need to worry about rollbacks? Nick: Our trail files contain committed data only and all data is sequential. So this is two important things. Because it contains committed data only, we don't need to worry about rollbacks. We also don't need to worry about position within that trail file because we know all data is sequential. And so as we're reading through the trail file, we know that anything that's written in a prior location in that trial file was committed prior to something else. And as we get into the recovery aspects of GoldenGate, this will all make a lot more sense. 08:13 Lois: Before we do that, can you tell us about the naming of trail files? Nick: The trail files as far as naming, because these do reside on the operating system, you start with a two-letter trail file abbreviation and then a nine-digit sequential value. So, you almost look at it as like an archive log from Oracle, where we have a prefix and then an affix, which is numeric. Same kind of thing. So, we have our two-letter, in this case, an ab, and then we have a nine-digit number. 08:47 Transform the way you work with Oracle Database 23ai! This cutting-edge technology brings the power of AI directly to your data, making it easier to build powerful applications and manage critical workloads. Want to learn more about Database 23ai? Visit mylearn.oracle.com to pick from our range of courses and enroll today! 09:12 Nikita: Welcome back! Ok, Nick. Let’s get into the GoldenGate recovery process. Nick: When we start looking at the GoldenGate recovery process, it essentially makes GoldenGate kind of point-in-time like. So on that source database, you have your extract process that's going to be capturing data from the transaction logs. In the case of Oracle, the Oracle Database is actually going to be reading those transaction logs from us and passing the change records directly to GoldenGate. We call them an LCR, Logical Change Record. And so the integrated extract and GoldenGate, the extract portion tells the database, hey, I'm now going to be interested in the following list of tables. And it gives a list of tables to that internal component, the log mining engine within the database. And it says, OK, I'm now pulling data for those tables and I'm going to send you those table changes. And so as the extract process gets sent those changes, it's going to have checkpoint information. So not only does it know where it was pulling data from out of that source database, but what it's also writing to the trail file. The trail files themselves are all sequential and they have only committed data, as we talked about earlier. The distribution service has checkpoint information that says, hey, I know where I'm reading from in the previous trail file, and I know what I've sent across the network. The receiver service is the same thing. It knows what it's receiving, as well as what it's written to the trail file and the target system. The replicat also has a checkpoint. It knows where it's reading from in the trail file, and then it knows what it's been applying into that target database. This is where things start to become a little complicated. Our replicat process in most cases are parallel, so it'll have multiple threads applying data into that target database. Each of those threads is applying different transactions. And because of the way that the parallelism works in the replicat process, you can actually get situations where one replicat thread might be applying a transaction higher than another thread. And so you can eliminate that sequential or serial aspect of it, and we can get very high throughput speeds to the replicat. But it means that the checkpoint needs to be kind of smart enough to know how to rebuild itself if something fails. 11:32 Lois: Ok, sorry Nick, but can you go through that again? Maybe we can work backwards this time? Nick: If the replicat process fails, when it comes back up, it's going to look to its checkpoint tables inside that target database. These checkpoint tables keep track of where each thread was at when it crashed. And so when the replicat process restarts, it goes, oh, I was applying these threads at this location in these SCNs. It'll then go and read from the trail file and say, hey, let me rebuild that data and it only applies transactions that it hasn't applied yet to that target system. There is a synchronized replicat command as well that will tell a crashed replicat to say, hey, bring all your threads up to the same high watermark. It does that process automatically as it restarts and continues normal replication. But there is an option to do it just by itself too. So that's how the replicat kind of repairs and recovers itself. It'll simply look at the trail files. Now, let's say that the replicat crashed, and it goes to read from the trail files when it restarts and that trail profile is missing. It'll actually communicate to the distribution, or excuse me, to the receiver service and say, hey, receiver service, I don't have this trail file. Can you bring it back for me? And the receiver service will communicate downstream and say, hey, distribution service, I need you to resend me trail find number 6. And so the distribution service will resend that trail file so that the replicat can reprocess it. So it's often nice to have redundant environments with GoldenGate so we can have those trail files kind of around for availability. 13:13 Nikita: What if one of these files gets corrupted? Nick: If one of those trail files is corrupt, let's say that a trail file on the target site became corrupt and the replicat can't read from it for one reason or another. Simply stop the replicat process, delete the corrupt trail file, restart the replicat process, and now it's going to rebuild that trail file from scratch based on the information from the source GoldenGate environment. And so it's very recoverable. Handles it all very well. 13:40 Nikita: And can the extract process bounce back in the same way? Nick: The extract process can also recover in a similar way. So if the extract process crashes, when it restarts itself, there's a number of things that it does. The first thing is it has to rebuild any open transactions. So it keeps all sorts of checkpoint information about the oldest transaction that it's keeping track of, any open transactions that haven't been committed, and any other transactions that have been committed that it's already written to the trail file. So as it's reprocessing that data, it knows exactly what it's committed to trail and what hasn't been committed. And there's a number of ways that it does this. There's two main components here. One of them is called bounded recovery. Bounded recovery will allow you to set a time limit on transactions that span a certain length of time that they'll actually get flushed out to disk on that GoldenGate Hub. And that way it'll reduce the amount of time it takes GoldenGate to restart the extract process. And the other component is cache manager. Cache manager stores uncommitted transactions. And so it's a very elegant way of rebuilding itself from any kind of failure. You can also set up restart profiles so that if any process does crash, the GoldenGate service manager can automatically restart that service an x number of times across y time span. So if I say, hey, if my extract crashes, then attempt to restart it 100 times every 5 seconds. So there's a lot of things that you can do there to make it really nice and automatic repair itself and automatically resilient. 15:18 Lois: Well, that brings us to the end of this episode. Thank you, Nick, for going through the security strategies and recovery processes in such detail. Next week, we’ll look at the installation of GoldenGate. Nikita: And if you want to learn more about the topics we discussed today, head over to mylearn.oracle.com and take a look at the Oracle GoldenGate 23ai Fundamentals course. Until next time, this is Nikita Abraham… Lois: And Lois Houston signing off! 15:44 That’s all for this episode of the Oracle University Podcast. 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GoldenGate 23ai: Terminology & Architecture
05/13/2025
GoldenGate 23ai: Terminology & Architecture
In this episode, Lois Houston and Nikita Abraham, along with Nick Wagner, focus on GoldenGate’s terminology and architectural evolution. Nick defines source and target systems, which are crucial for data replication, and then moves on to explain the data extraction and replication processes. He also talks about the new microservices architecture, which replaces the classic architecture, offering benefits like simplified management, enhanced security, and a user-friendly interface. Nick highlights how this architecture facilitates easy upgrades and provides a streamlined experience for administrators. Oracle GoldenGate 23ai: Fundamentals: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. --------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Nikita: Welcome to the Oracle University Podcast! I’m Nikita Abraham, Team Lead of Editorial Services with Oracle University, and with me is Lois Houston: Director of Innovation Programs. Lois: Hi there! Thanks for joining us again as we make our way through Oracle GoldenGate 23ai. Last week, we discussed all the new features introduced in 23ai and today, we’ll move on to the terminology, the different processes and what they do, and the architecture of the product at a high level. 00:56 Nikita: Back with us is Nick Wagner, Senior Director of Product Management for Oracle GoldenGate. Hi Nick! Let’s get into some of the terminology. What do we actually call stuff in GoldenGate? Nick: Within GoldenGate, we have our source systems and our target systems. The source is where we're going to be capturing data from, the targets, where we're going to be applying data into. And when we start talking about things like active-active or setting up GoldenGate for high availability, where your source can also be your target, it does become a little bit more complex. And so in some of those cases, we might refer to things as East and West, or America and Europe, or different versions of that. We also have a couple of different things within the product itself. We have what we call our Extract and our Replicat. The Extract is going to be the process that pulls the data out of the database, our capture technology. Our Replicat’s going to be the one that applies the data into the target system, or you can also look at it as a push technology. We have what we call our Distribution Path. Our Distribution Path is going to be how we're sending the data across the network. A lot of times when customers run GoldenGate, they don't have the luxury of just having a single server of GoldenGate that can pull data from one database and push data into another one. They need to set up multiple hops of that data. And so in that case, we would use what we call a Distribution Path to send that data from one system to the next. We also have what we call a Target Initiated Path. It's kind of a subset of your Distribution Path, but it allows you to communicate from a less secure environment into a more secure environment. 02:33 Lois: Nick, what about parameter names. I’ve seen them in uppercase…title case…does that matter? Nick: GoldenGate has a lot of parameters. This is something you'll see all over the place within GoldenGate itself. These parameters are in your Extract and Replicat parameter files during your distribution path parameter files. Parameters for GoldenGate are case insensitive. Within your own environments, you can set it up to have lowercase, mixed case, whatever you want, but just be aware that they are case insensitive. GoldenGate doesn't care, it's just for readability. And then we also have something called trail files. Trail files is where GoldenGate stores all the data before we're able to apply it into that target system. Think about it as our queuing mechanism, and we're queuing everything outside the database so that we're not overloading those database environments. And that's some of the terminology for the product itself. We also have microservices within GoldenGate. 03:31 Nikita: And at the heart of everything is the Service Manager, right? Talk to us about what it is and what it does. Nick: The service manager is responsible for making sure that everything else is up and running. If you are familiar with GoldenGate classic architecture, this is kind of similar to a GoldenGate manager where that process was there to make sure that processes were running the trail files, or excuse me, that certain error logs were getting written out. If a process went down, the manager would restart that process. The service manager is performing a lot of those same functions. Now attached to the service manager, we have our configuration service. This is new in GoldenGate 23ai. This configuration service is going to allow you to set up GoldenGate for highly available environments. So you can build HA into GoldenGate itself using the configuration service. 04:22 Lois: And what does this configuration service do? Nick: This configuration service essentially moves the checkpoint files that used to be on disk into a database so that everything can be stored inside of a database. Also attached to the service manager, we have the performance metric service. This is a service that is going to be gathering all the performance metrics of GoldenGate. So it's going to tell you how fast things are going, what the latencies are, how many bytes per second we're reading from, the transaction logs or writing to our trail files. How quickly a distribution path is sending data across a network. If you want to know any of your lag information, you'll get it from the performance metrics server. We also have the receiver service and the distribution service. These two work hand in hand to establish network communication between two GoldenGate environments. So on what we call our source system, we have a distribution service that's going to send the data to our target system. On the target system, a receiver service is going to receive that data and then rewrite the trail files. We also have the administration service that's responsible for authentication and authorization of the users, as well as making sure that people have access to the right information. 05:33 Nikita: Ok. Moving on the deployment, how is GoldenGate actually deployed, Nick? Nick: GoldenGate is kinda nice. So the way that the product is installed is you install the GoldenGate environment and that's what we call our service manager deployment under a specific GoldenGate home. So the software binaries themselves get installed under a home, we'll say U01/OGG23AI. Now once I've installed GoldenGate once, that's my OGG home. I can now have any number of service managers and deployments tied to that same home. 06:11 Lois: Ok, let’s work with an example to make this simpler. Let’s say I've got a service manager that's going be responsible for three different deployments: Accounting, Finance, and Sales. Nick: Each of these deployments is going to reside in its own directory. Each of these deployments is going to have its own set of microservices. And so this also means that each of these deployments can have their own set of users. So the people that access the GoldenGate accounting deployment can be different than the ones that access the sales deployment. This means with this distribution of roles that I can have somebody come in and administer the sales database, but they wouldn't have any information or any access to accounting or finance. And this is very important, it allows you to really pull that information apart and separate it. Each of these environments also has their own set of parameter files, Extract process, Replicat process, distribution services, and everything. So it's a very nice way of splitting things up, but all having them tied to the same GoldenGate home system. And this home is very important. So I can take a deployment, let's say my finance deployment, and if I want to move it to a new GoldenGate home and that GoldenGate home is a different version, like let's say that my original home is 23.4, my new GoldenGate home is 23.7, I simply stop that GoldenGate deployment. I stopped at a finance deployment. I changed its OGG home from 23.4 to 23.7. I restart the deployment, that deployment is automatically upgraded to the new environment and attached to the new system. So it makes upgrading very, very simple, very easy, very elegant. 07:53 Nikita: Ok. So, we’ve spoken about the services…some of the terminology. Let’s get into the architecture next. Nick: So when we talk about the architecture for GoldenGate, we used to have two different architectures. We had a classic architecture and a microservices architecture. Classic architecture was something that's been around since the very beginning of GoldenGate in the late '90s. We announced that, that architecture was deprecated in 19c. And Oracle deprecated means that feature is no longer going to be enhanced and it'll be patched selectively. And at some point in the future, it'll be entirely desupported. Well, GoldenGate 23ai is that future. And so in 23ai, the classic architecture is desupported, that means that it's no longer in the build at all. And so it's just microservices architecture. 08:41 Lois: Is there a tool to assist with this migration? Nick: We do have a migration utility that will convert an old classic architecture into the new microservices architecture. But there is quite a bit of learning curve to the new microservices architecture. So it's important that we go through how it works in the changes. 09:04 Are you looking to optimize your implementation strategies and improve efficiency? We have a solution for you! Our new Oracle Fusion Cloud Applications Foundations training and certification program. You’ll learn to leverage Oracle Modern Best Practice (OMBP) to re-imagine business processes using advanced technologies in Oracle Fusion Cloud Applications such as AI, mobile, analytics, and more. Visit mylearn.oracle.com to get started today. 09:37 Nikita: Welcome back! Nick, what are the benefits of this microservices architecture? Nick: It's got that simplified lifecycle for patching and upgrading. A lot of the GoldenGate patches that you get, especially these bundle patches, are complete installs as well. So you can go into My Oracle Support and download a complete install of a patch and that way, you don't have to use old patch to apply them. The only time you'll be using old patch is for one-off patches or smaller patches that need to be applied to your GoldenGate system. The microservices product has the same trusted Capture and Apply process that Classic did. There's almost no changes between the two except on how they communicate with their parent processes. And so the same logic that you use to pull data from Oracle or to apply data into Oracle is all the same. 10:25 Lois: And has the interface been upgraded as well? Nick: We've added a really nice, easy to use web interface for the microservices version of GoldenGate. Not only is this web interface work with all your standard browsers, but it's also mobile friendly too. So I can actually control and administer GoldenGate right through my mobile device. It also has new secure remote administration. This is something that the classic architecture was really missing. And so in the classic architecture, to use the command line interface, you had to log into the database server where GoldenGate was installed. Now, the command line interface, as well as the web interface and the REST API, all use remote administration and authentication. So that means that I can install the new command line interface or what we call admin client on my laptop locally and I can connect to any GoldenGate deployment as long as I have the username and password for that deployment. It's also more secure. GoldenGate microservices can also be deployed on premise or in OCI as a service and now also on these third-party clouds like Azure and Google Cloud. And it's also easier for developers to integrate in with the APIs themselves. Everything that GoldenGate does through the admin client as well as the web UI can all be traced. The REST API calls for GoldenGate are all fully published so you can get them right directly from the documentation, you can build your own web interface if you want to. So it makes it very easy. The REST APIs are also streamlined. With a single REST API call, I can do something like add an Extract process, create it, set up my parameter file, and set up the trail files all with a single API command. Whereas in the past, it would require multiple command line interface commands to do that same thing. So it's extremely elegant, very advanced. 12:16 Nikita: What does the microservices architecture look like? I know it’s a bit complicated when we’re not actually looking at a diagram of it, but just a high level, can you explain the different parts of it? Nick: It's pretty straightforward. But essentially what you've got on each system is a service manager. That service manager is then going to have a number of processes or services beneath it. It'll have the configuration service that stores the checkpoint information for GoldenGate. It'll have the administrative service for the authentication and users, the distribution service to send the data across a network, a receiver service to receive that information, performance metrics to get the performance statistics out of GoldenGate. And then of course, you also have your Extracts and Replicats that capture and apply technology. Each of those Extracts and Replicats will then connect to a database on the Extract side of things. That Extract is going to write to trail files. Those trail files are then going to be sent across the network where they're rebuilt on the target system and the Replicat’s going to consume them and apply them into the target database. So the Replicat behaves almost like an end user. So it's taking that trail file data and simply converting it to DML operations, insert, update, delete, or a DDL operation in the case of Oracle, alter table, create table, et cetera, to go into that target database. 13:39 Lois: To look at a diagram of this architecture and learn about it in more detail, check out the Oracle GoldenGate 23ai Fundamentals course on mylearn.oracle.com. So, Nick, if I’m looking to deploy GoldenGate, what should I primarily keep in mind? Nick: So as you go to install GoldenGate and you look at a deployment, there's a couple of important environment variables that you want to make sure you're aware of. So one of the first ones is your OGG_Home. This environment variable is extremely important. This is the location of the GoldenGate software itself. And I want to stress how important it is to always use version numbers when you're setting up your GoldenGate home. When you go to install the software, if you're installing GoldenGate 23.5, use 23.5 within the home directory structure. If you're installing GoldenGate 23.7, use 23.7 inside that directory structure. 14:33 Nikita: Right… that way I’ll always know which versions are which, and it’ll make it really easy to upgrade and move from one version to the next. Ok, got it. What else, Nick? Nick: There's a couple other important directories. You have your OGG_ETC_HOME. This is where things like the configuration files are going to reside, parameter files, all your certificates for security, including the wallets where we store the credentials for not only the database accounts, but also for the GoldenGate user accounts as well. We have our GoldenGate variable home directory or VAR home. This is where all the GoldenGate log files are residing. And these are the log files that allow you to see what's going on in GoldenGate for auditing purposes. Anytime anybody makes a change to GoldenGate, you're going to see information go into the log files on what was happening and how it was working and what they did, what time they did, what command they issued. Another big important feature about these log files is it also gives you error information and troubleshooting details. So if you ever need to find out what happened in GoldenGate, what went wrong, you would look at these log files to find out that information. And then you also have your OGG_DATA_HOME. This is where those trail files are going to go. Essentially, this is kind of the queuing or overflow for GoldenGate. There's a couple of other additional components. We've got the admin client. This is our command line utility. If you don't want to use a web browser or prefer a command line utility, you can use the admin client. The admin client is also fully scriptable. So if you wanted to write scripts that would go off and automate things in GoldenGate, you can do that. A lot of customers did that with GGSCI in the classic architecture. You can do the same thing now with the admin client. The other component is the microservices security authentication and authorization services. These handle communication security, especially making sure that any passwords or usernames and everything like that is all encrypted. And instead of using an actual username and password, everything through the product is going to be done through an alias. And then it also handles all the authorization authentication, permissions, user accountability, and roles within GoldenGate. 16:39 Lois: Anything else you’d like to talk about before we wrap up for today, Nick? Nick: I also wanted to take a minute to talk about the REST API. All the microservices provide REST APIs to administer them and all of these are fully documented. They can be used by any client that can make REST API calls. So if you wanted to use Python, cURL, a web browser, you can do that as well. They're all just HTTP or HTTPS calls, get, put, patch, the standard REST API standards. And then GoldenGate does provide our admin client as well as a WebUI that use these REST APIs under the covers if you ever wanted to get a more advanced look at how it works. 17:18 Nikita: Well, that’s all the time we have for today. Thanks for joining us, Nick. Lois: Yes, thanks Nick. We look forward to having you back next week to talk with us about security strategies and data recovery. Nikita: And if you want to learn more about the topics we discussed today, head over to mylearn.oracle.com and take a look at the Oracle GoldenGate 23ai Fundamentals course. Until next time, this is Nikita Abraham… Lois: And Lois Houston, signing off! 17:43 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. 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Oracle GoldenGate 23ai: New Features & Product Family
05/06/2025
Oracle GoldenGate 23ai: New Features & Product Family
In this episode, Lois Houston and Nikita Abraham continue their deep dive into Oracle GoldenGate 23ai, focusing on its evolution and the extensive features it offers. They are joined once again by Nick Wagner, who provides valuable insights into the product's journey. Nick talks about the various iterations of Oracle GoldenGate, highlighting the significant advancements from version 12c to the latest 23ai release. The discussion then shifts to the extensive new features in 23ai, including AI-related capabilities, UI enhancements, and database function integration. Oracle GoldenGate 23ai: Fundamentals: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ----------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast! I’m Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hi everyone! Last week, we introduced Oracle GoldenGate and its capabilities, and also spoke about GoldenGate 23ai. In today’s episode, we’ll talk about the various iterations of Oracle GoldenGate since its inception. And we’ll also take a look at some new features and the Oracle GoldenGate product family. 00:57 Lois: And we have Nick Wagner back with us. Nick is a Senior Director of Product Management for GoldenGate at Oracle. Hi Nick! I think the last time we had an Oracle University course was when Oracle GoldenGate 12c was out. I’m sure there’s been a lot of advancements since then. Can you walk us through those? Nick: GoldenGate 12.3 introduced the microservices architecture. GoldenGate 18c introduced support for Oracle Autonomous Data Warehouse and Autonomous Transaction Processing Databases. In GoldenGate 19c, we added the ability to do cross endian remote capture for Oracle, making it easier to set up the GoldenGate OCI service to capture from environments like Solaris, Spark, and HP-UX and replicate into the Cloud. Also, GoldenGate 19c introduced a simpler process for upgrades and installation of GoldenGate where we released something called a unified build. This means that when you install GoldenGate for a particular database, you don't need to worry about the database version when you install GoldenGate. Prior to this, you would have to install a version-specific and database-specific version of GoldenGate. So this really simplified that whole process. In GoldenGate 23ai, which is where we are now, this really is a huge release. 02:16 Nikita: Yeah, we covered some of the distributed AI features and high availability environments in our last episode. But can you give us an overview of everything that’s in the 23ai release? I know there’s a lot to get into but maybe you could highlight just the major ones? Nick: Within the AI and streaming environments, we've got interoperability for database vector types, heterogeneous capture and apply as well. Again, this is not just replication between Oracle-to-Oracle vector or Postgres to Postgres vector, it is heterogeneous just like the rest of GoldenGate. The entire UI has been redesigned and optimized for high speed. And so we have a lot of customers that have dozens and dozens of extracts and replicats and processes running and it was taking a long time for the UI to refresh those and to show what's going on within those systems. So the UI has been optimized to be able to handle those environments much better. We now have the ability to call database functions directly from call map. And so when you do transformation with GoldenGate, we have about 50 or 60 built-in transformation routines for string conversion, arithmetic operation, date manipulation. But we never had the ability to directly call a database function. 03:28 Lois: And now we do? Nick: So now you can actually call that database function, database stored procedure, database package, return a value and that can be used for transformation within GoldenGate. We have integration with identity providers, being able to use token-based authentication and integrate in with things like Azure Active Directory and your other single sign-on for the GoldenGate product itself. Within Oracle 23ai, there's a number of new features. One of those cool features is something called lock-free reservation columns. So this allows you to have a row, a single row within a table and you can identify a column within that row that's like an inventory column. And you can have multiple different users and multiple different transactions all updating that column within that same exact row at that same time. So you no longer have row-level locking for these reservation columns. And it allows you to do things like shopping carts very easily. If I have 500 widgets to sell, I'm going to let any number of transactions come in and subtract from that inventory column. And then once it gets below a certain point, then I'll start enforcing that row-level locking. 04:43 Lois: That’s really cool… Nick: The one key thing that I wanted to mention here is that because of the way that the lock-free reservations work, you can have multiple transactions open on the same row. This is only supported for Oracle to Oracle. You need to have that same lock-free reservation data type and availability on that target system if GoldenGate is going to replicate into it. 05:05 Nikita: Are there any new features related to the diagnosability and observability of GoldenGate? Nick: We've improved the AWR reports in Oracle 23ai. There's now seven sections that are specific to Oracle GoldenGate to allow you to really go in and see exactly what the GoldenGate processes are doing and how they're behaving inside the database itself. And there's a Replication Performance Advisor package inside that database, and that's been integrated into the Web UI as well. So now you can actually get information out of the replication advisor package in Oracle directly from the UI without having to log into the database and try to run any database procedures to get it. We've also added the ability to support a per-PDB Extract. So in the past, when GoldenGate would run on a multitenant database, a multitenant database in Oracle, all the redo data from any pluggable database gets sent to that one redo stream. And so you would have to configure GoldenGate at the container or root level and it would be able to access anything at any PDB. Now, there's better security and better performance by doing what we call per-PDB Extract. And this means that for a single pluggable database, I can have an extract that runs at that database level that's going to capture information just from that pluggable database. 06:22 Lois And what about non-Oracle environments, Nick? Nick: We've also enhanced the non-Oracle environments as well. For example, in Postgres, we've added support for precise instantiation using Postgres snapshots. This eliminates the need to handle collisions when you're doing Postgres to Postgres replication and initial instantiation. On the GoldenGate for big data side, we've renamed that product more aptly to distributed applications in analytics, which is really what it does, and we've added a whole bunch of new features here too. The ability to move data into Databricks, doing Google Pub/Sub delivery. We now have support for XAG within the GoldenGate for distributed applications and analytics. What that means is that now you can follow all of our MAA best practices for GoldenGate for Oracle, but it also works for the DAA product as well, meaning that if it's running on one node of a cluster and that node fails, it'll restart itself on another node in the cluster. We've also added the ability to deliver data to Redis, Google BigQuery, stage and merge functionality for better performance into the BigQuery product. And then we've added a completely new feature, and this is something called streaming data and apps and we're calling it AsyncAPI and CloudEvent data streaming. It's a long name, but what that means is that we now have the ability to publish changes from a GoldenGate trail file out to end users. And so this allows through the Web UI or through the REST API, you can now come into GoldenGate and through the distributed applications and analytics product, actually set up a subscription to a GoldenGate trail file. And so this allows us to push data into messaging environments, or you can simply subscribe to changes and it doesn't have to be the whole trail file, it can just be a subset. You can specify exactly which tables and you can put filters on that. You can also set up your topologies as well. So, it's a really cool feature that we've added here. 08:26 Nikita: Ok, you’ve given us a lot of updates about what GoldenGate can support. But can we also get some specifics? Nick: So as far as what we have, on the Oracle Database side, there's a ton of different Oracle databases we support, including the Autonomous Databases and all the different flavors of them, your Oracle Database Appliance, your Base Database Service within OCI, your of course, Standard and Enterprise Edition, as well as all the different flavors of Exadata, are all supported with GoldenGate. This is all for capture and delivery. And this is all versions as well. GoldenGate supports Oracle 23ai and below. We also have a ton of non-Oracle databases in different Cloud stores. On an non-Oracle side, we support everything from application-specific databases like FairCom DB, all the way to more advanced applications like Snowflake, which there's a vast user base for that. We also support a lot of different cloud stores and these again, are non-Oracle, nonrelational systems, or they can be relational databases. We also support a lot of big data platforms and this is part of the distributed applications and analytics side of things where you have the ability to replicate to different Apache environments, different Cloudera environments. We also support a number of open-source systems, including things like Apache Cassandra, MySQL Community Edition, a lot of different Postgres open source databases along with MariaDB. And then we have a bunch of streaming event products, NoSQL data stores, and even Oracle applications that we support. So there's absolutely a ton of different environments that GoldenGate supports. There are additional Oracle databases that we support and this includes the Oracle Metadata Service, as well as Oracle MySQL, including MySQL HeatWave. Oracle also has Oracle NoSQL Spatial and Graph and times 10 products, which again are all supported by GoldenGate. 10:23 Lois: Wow, that’s a lot of information! Nick: One of the things that we didn't really cover was the different SaaS applications, which we've got like Cerner, Fusion Cloud, Hospitality, Retail, MICROS, Oracle Transportation, JD Edwards, Siebel, and on and on and on. And again, because of the nature of GoldenGate, it's heterogeneous. Any source can talk to any target. And so it doesn't have to be, oh, I'm pulling from Oracle Fusion Cloud, that means I have to go to an Oracle Database on the target, not necessarily. 10:51 Lois: So, there’s really a massive amount of flexibility built into the system. 11:00 Unlock the power of AI Vector Search with our new course and certification. Get more accurate search results, handle complex datasets easily, and supercharge your data-driven decisions. From now through May 15, 2025, we are waiving the certification exam fee (valued at $245). Visit mylearn.oracle.com to enroll. 11:26 Nikita: Welcome back! Now that we’ve gone through the base product, what other features or products are in the GoldenGate family itself, Nick? Nick: So we have quite a few. We've kind of touched already on GoldenGate for Oracle databases and non-Oracle databases. We also have something called GoldenGate for Mainframe, which right now is covered under the GoldenGate for non-Oracle, but there is a licensing difference there. So that's something to be aware of. We also have the OCI GoldenGate product. We are announcing and we have announced that OCI GoldenGate will also be made available as part of the Oracle Database@Azure and Oracle Database@ Google Cloud partnerships. And then you'll be able to use that vendor's cloud credits to actually pay for the OCI GoldenGate product. One of the cool things about this is it will have full feature parity with OCI GoldenGate running in OCI. So all the same features, all the same sources and targets, all the same topologies be able to migrate data in and out of those clouds at will, just like you do with OCI GoldenGate today running in OCI. We have Oracle GoldenGate Free. This is a completely free edition of GoldenGate to use. It is limited on the number of platforms that it supports as far as sources and targets and the size of the database. 12:45 Lois: But it's a great way for developers to really experience GoldenGate without worrying about a license, right? What’s next, Nick? Nick: We have GoldenGate for Distributed Applications and Analytics, which was formerly called GoldenGate for big data, and that allows us to do all the streaming. That's also where the GoldenGate AsyncAPI integration is done. So in order to publish the GoldenGate trail files or allow people to subscribe to them, it would be covered under the Oracle GoldenGate Distributed Applications and Analytics license. We also have OCI GoldenGate Marketplace, which allows you to run essentially the on-premises version of GoldenGate but within OCI. So a little bit more flexibility there. It also has a hub architecture. So if you need that 99.99% availability, you can get it within the OCI Marketplace environment. We have GoldenGate for Oracle Enterprise Manager Cloud Control, which used to be called Oracle Enterprise Manager. And this allows you to use Enterprise Manager Cloud Control to get all the statistics and details about GoldenGate. So all the reporting information, all the analytics, all the statistics, how fast GoldenGate is replicating, what's the lag, what's the performance of each of the processes, how much data am I sending across a network. All that's available within the plug-in. We also have Oracle GoldenGate Veridata. This is a nice utility and tool that allows you to compare two databases, whether or not GoldenGate is running between them and actually tell you, hey, these two systems are out of sync. And if they are out of sync, it actually allows you to repair the data too. 14:25 Nikita: That’s really valuable…. Nick: And it does this comparison without locking the source or the target tables. The other really cool thing about Veridata is it does this while there's data in flight. So let's say that the GoldenGate lag is 15 or 20 seconds and I want to compare this table that has 10 million rows in it. The Veridata product will go out, run its comparison once. Once that comparison is done the first time, it's then going to have a list of rows that are potentially out of sync. Well, some of those rows could have been moved over or could have been modified during that 10 to 15 second window. And so the next time you run Veridata, it's actually going to go through. It's going to check just those rows that were potentially out of sync to see if they're really out of sync or not. And if it comes back and says, hey, out of those potential rows, there's two out of sync, it'll actually produce a script that allows you to resynchronize those systems and repair them. So it's a very cool product. 15:19 Nikita: What about GoldenGate Stream Analytics? I know you mentioned it in the last episode, but in the context of this discussion, can you tell us a little more about it? Nick: This is the ability to essentially stream data from a GoldenGate trail file, and they do a real time analytics on it. And also things like geofencing or real-time series analysis of it. 15:40 Lois: Could you give us an example of this? Nick: If I'm working in tracking stock market information and stocks, it's not really that important on how much or how far down a stock goes. What's really important is how quickly did that stock rise or how quickly did that stock fall. And that's something that GoldenGate Stream Analytics product can do. Another thing that it's very valuable for is the geofencing. I can have an application on my phone and I can track where the user is based on that application and all that information goes into a database. I can then use the geofencing tool to say that, hey, if one of those users on that app gets within a certain distance of one of my brick-and-mortar stores, I can actually send them a push notification to say, hey, come on in and you can order your favorite drink just by clicking Yes, and we'll have it ready for you. And so there's a lot of things that you can do there to help upsell your customers and to get more revenue just through GoldenGate itself. And then we also have a GoldenGate Migration Utility, which allows customers to migrate from the classic architecture into the microservices architecture. 16:44 Nikita: Thanks Nick for that comprehensive overview. Lois: In our next episode, we’ll have Nick back with us to talk about commonly used terminology and the GoldenGate architecture. And if you want to learn more about what we discussed today, visit mylearn.oracle.com and take a look at the Oracle GoldenGate 23ai Fundamentals course. Until next time, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 17:10 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
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What is Oracle GoldenGate 23ai?
04/29/2025
What is Oracle GoldenGate 23ai?
In a new season of the Oracle University Podcast, Lois Houston and Nikita Abraham dive into the world of Oracle GoldenGate 23ai, a cutting-edge software solution for data management. They are joined by Nick Wagner, a seasoned expert in database replication, who provides a comprehensive overview of this powerful tool. Nick highlights GoldenGate's ability to ensure continuous operations by efficiently moving data between databases and platforms with minimal overhead. He emphasizes its role in enabling real-time analytics, enhancing data security, and reducing costs by offloading data to low-cost hardware. The discussion also covers GoldenGate's role in facilitating data sharing, improving operational efficiency, and reducing downtime during outages. Oracle GoldenGate 23ai: Fundamentals: Oracle University Learning Community: LinkedIn: X: Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. --------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started! 00:25 Nikita: Welcome to the Oracle University Podcast! I’m Nikita Abraham, Team Lead: Editorial Services with Oracle University, and with me is Lois Houston: Director of Innovation Programs. Lois: Hi everyone! Welcome to a new season of the podcast. This time, we’re focusing on the fundamentals of Oracle GoldenGate. Oracle GoldenGate helps organizations manage and synchronize their data across diverse systems and databases in real time. And with the new Oracle GoldenGate 23ai release, we’ll uncover the latest innovations and features that empower businesses to make the most of their data. Nikita: Taking us through this is Nick Wagner, Senior Director of Product Management for Oracle GoldenGate. He’s been doing database replication for about 25 years and has been focused on GoldenGate on and off for about 20 of those years. 01:18 Lois: In today’s episode, we’ll ask Nick to give us a general overview of the product, along with some use cases and benefits. Hi Nick! To start with, why do customers need GoldenGate? Nick: Well, it delivers continuous operations, being able to continuously move data from one database to another database or data platform in efficiently and a high-speed manner, and it does this with very low overhead. Almost all the GoldenGate environments use transaction logs to pull the data out of the system, so we're not creating any additional triggers or very little overhead on that source system. GoldenGate can also enable real-time analytics, being able to pull data from all these different databases and move them into your analytics system in real time can improve the value that those analytics systems provide. Being able to do real-time statistics and analysis of that data within those high-performance custom environments is really important. 02:13 Nikita: Does it offer any benefits in terms of cost? Nick: GoldenGate can also lower IT costs. A lot of times people run these massive OLTP databases, and they are running reporting in those same systems. With GoldenGate, you can offload some of the data or all the data to a low-cost commodity hardware where you can then run the reports on that other system. So, this way, you can get back that performance on the OLTP system, while at the same time optimizing your reporting environment for those long running reports. You can improve efficiencies and reduce risks. Being able to reduce the amount of downtime during planned and unplanned outages can really make a big benefit to the overall operational efficiencies of your company. 02:54 Nikita: What about when it comes to data sharing and data security? Nick: You can also reduce barriers to data sharing. Being able to pull subsets of data, or just specific pieces of data out of a production database and move it to the team or to the group that needs that information in real time is very important. And it also protects the security of your data by only moving in the information that they need and not the entire database. It also provides extensibility and flexibility, being able to support multiple different replication topologies and architectures. 03:24 Lois: Can you tell us about some of the use cases of GoldenGate? Where does GoldenGate truly shine? Nick: Some of the more traditional use cases of GoldenGate include use within the multicloud fabric. Within a multicloud fabric, this essentially means that GoldenGate can replicate data between on-premise environments, within cloud environments, or hybrid, cloud to on-premise, on-premise to cloud, or even within multiple clouds. So, you can move data from AWS to Azure to OCI. You can also move between the systems themselves, so you don't have to use the same database in all the different clouds. For example, if you wanted to move data from AWS Postgres into Oracle running in OCI, you can do that using Oracle GoldenGate. We also support maximum availability architectures. And so, there's a lot of different use cases here, but primarily geared around reducing your recovery point objective and recovery time objective. 04:20 Lois: Ah, reducing RPO and RTO. That must have a significant advantage for the customer, right? Nick: So, reducing your RPO and RTO allows you to take advantage of some of the benefits of GoldenGate, being able to do active-active replication, being able to set up GoldenGate for high availability, real-time failover, and it can augment your active Data Guard and Data Guard configuration. So, a lot of times GoldenGate is used within Oracle's maximum availability architecture platinum tier level of replication, which means that at that point you've got lots of different capabilities within the Oracle Database itself. But to help eke out that last little bit of high availability, you want to set up an active-active environment with GoldenGate to really get true zero RPO and RTO. GoldenGate can also be used for data offloading and data hubs. Being able to pull data from one or more source systems and move it into a data hub, or into a data warehouse for your operational reporting. This could also be your analytics environment too. 05:22 Nikita: Does GoldenGate support online migrations? Nick: In fact, a lot of companies actually get started in GoldenGate by doing a migration from one platform to another. Now, these don't even have to be something as complex as going from one database like a DB2 on-premise into an Oracle on OCI, it could even be simple migrations. A lot of times doing something like a major application or a major database version upgrade is going to take downtime on that production system. You can use GoldenGate to eliminate that downtime. So this could be going from Oracle 19c to Oracle 23ai, or going from application version 1.0 to application version 2.0, because GoldenGate can do the transformation between the different application schemas. You can use GoldenGate to migrate your database from on premise into the cloud with no downtime as well. We also support real-time analytic feeds, being able to go from multiple databases, not only those on premise, but being able to pull information from different SaaS applications inside of OCI and move it to your different analytic systems. And then, of course, we also have the ability to stream events and analytics within GoldenGate itself. 06:34 Lois: Let's move on to the various topologies supported by GoldenGate. I know GoldenGate supports many different platforms and can be used with just about any database. Nick: This first layer of topologies is what we usually consider relational database topologies. And so this would be moving data from Oracle to Oracle, Postgres to Oracle, Sybase to SQL Server, a lot of different types of databases. So the first architecture would be unidirectional. This is replicating from one source to one target. You can do this for reporting. If I wanted to offload some reports into another server, I can go ahead and do that using GoldenGate. I can replicate the entire database or just a subset of tables. I can also set up GoldenGate for bidirectional, and this is what I want to set up GoldenGate for something like high availability. So in the event that one of the servers crashes, I can almost immediately reconnect my users to the other system. And that almost immediately depends on the amount of latency that GoldenGate has at that time. So a typical latency is anywhere from 3 to 6 seconds. So after that primary system fails, I can reconnect my users to the other system in 3 to 6 seconds. And I can do that because as GoldenGate’s applying data into that target database, that target system is already open for read and write activity. GoldenGate is just another user connecting in issuing DML operations, and so it makes that failover time very low. 07:59 Nikita: Ok…If you can get it down to 3 to 6 seconds, can you bring it down to zero? Like zero failover time? Nick: That's the next topology, which is active-active. And in this scenario, all servers are read/write all at the same time and all available for user activity. And you can do multiple topologies with this as well. You can do a mesh architecture, which is where every server talks to every other server. This works really well for 2, 3, 4, maybe even 5 environments, but when you get beyond that, having every server communicate with every other server can get a little complex. And so at that point we start looking at doing what we call a hub and spoke architecture, where we have lots of different spokes. At the end of each spoke is a read/write database, and then those communicate with a hub. So any change that happens on one spoke gets sent into the hub, and then from the hub it gets sent out to all the other spokes. And through that architecture, it allows you to really scale up your environments. We have customers that are doing up to 150 spokes within that hub architecture. Within active-active replication as well, we can do conflict detection and resolution, which means that if two users modify the same row on two different systems, GoldenGate can actually determine that there was an issue with that and determine what user wins or which row change wins, which is extremely important when doing active-active replication. And this means that if one of those systems fails, there is no downtime when you switch your users to another active system because it's already available for activity and ready to go. 09:35 Lois: Wow, that’s fantastic. Ok, tell us more about the topologies. Nick: GoldenGate can do other things like broadcast, sending data from one system to multiple systems, or many to one as far as consolidation. We can also do cascading replication, so when data moves from one environment that GoldenGate is replicating into another environment that GoldenGate is replicating. By default, we ignore all of our own transactions. But there's actually a toggle switch that you can flip that says, hey, GoldenGate, even though you wrote that data into that database, still push it on to the next system. And then of course, we can also do distribution of data, and this is more like moving data from a relational database into something like a Kafka topic or a JMS queue or into some messaging service. 10:24 Raise your game with the Oracle Cloud Applications skills challenge. Get free training on Oracle Fusion Cloud Applications, Oracle Modern Best Practice, and Oracle Cloud Success Navigator. Pass the free Oracle Fusion Cloud Foundations Associate exam to earn a Foundations Associate certification. Plus, there’s a chance to win awards and prizes throughout the challenge! What are you waiting for? Join the challenge today by visiting visit oracle.com/education. 10:58 Nikita: Welcome back! Nick, does GoldenGate also have nonrelational capabilities? Nick: We have a number of nonrelational replication events in topologies as well. This includes things like data lake ingestion and streaming ingestion, being able to move data and data objects from these different relational database platforms into data lakes and into these streaming systems where you can run analytics on them and run reports. We can also do cloud ingestion, being able to move data from these databases into different cloud environments. And this is not only just moving it into relational databases with those clouds, but also their data lakes and data fabrics. 11:38 Lois: You mentioned a messaging service earlier. Can you tell us more about that? Nick: Messaging replication is also possible. So we can actually capture from things like messaging systems like Kafka Connect and JMS, replicate that into a relational data, or simply stream it into another environment. We also support NoSQL replication, being able to capture from MongoDB and replicate it onto another MongoDB for high availability or disaster recovery, or simply into any other system. 12:06 Nikita: I see. And is there any integration with a customer’s SaaS applications? Nick: GoldenGate also supports a number of different OCI SaaS applications. And so a lot of these different applications like Oracle Financials Fusion, Oracle Transportation Management, they all have GoldenGate built under the covers and can be enabled with a flag that you can actually have that data sent out to your other GoldenGate environment. So you can actually subscribe to changes that are happening in these other systems with very little overhead. And then of course, we have event processing and analytics, and this is the final topology or flexibility within GoldenGate itself. And this is being able to push data through data pipelines, doing data transformations. GoldenGate is not an ETL tool, but it can do row-level transformation and row-level filtering. 12:55 Lois: Are there integrations offered by Oracle GoldenGate in automation and artificial intelligence? Nick: We can do time series analysis and geofencing using the GoldenGate Stream Analytics product. It allows you to actually do real time analysis and time series analysis on data as it flows through the GoldenGate trails. And then that same product, the GoldenGate Stream Analytics, can then take the data and move it to predictive analytics, where you can run MML on it, or ONNX or other Spark-type technologies and do real-time analysis and AI on that information as it's flowing through. 13:29 Nikita: So, GoldenGate is extremely flexible. And given Oracle's focus on integrating AI into its product portfolio, what about GoldenGate? Does it offer any AI-related features, especially since the product name has “23ai” in it? Nick: With the advent of Oracle GoldenGate 23ai, it's one of the two products at this point that has the AI moniker at Oracle. Oracle Database 23ai also has it, and that means that we actually do stuff with AI. So the Oracle GoldenGate product can actually capture vectors from databases like MySQL HeatWave, Postgres using pgvector, which includes things like AlloyDB, Amazon RDS Postgres, Aurora Postgres. We can also replicate data into Elasticsearch and OpenSearch, or if the data is using vectors within OCI or the Oracle Database itself. So GoldenGate can be used for a number of things here. The first one is being able to migrate vectors into the Oracle Database. So if you're using something like Postgres, MySQL, and you want to migrate the vector information into the Oracle Database, you can. Now one thing to keep in mind here is a vector is oftentimes like a GPS coordinate. So if I need to know the GPS coordinates of Austin, Texas, I can put in a latitude and longitude and it will give me the GPS coordinates of a building within that city. But if I also need to know the altitude of that same building, well, that's going to be a different algorithm. And GoldenGate and replicating vectors is the same way. When you create a vector, it's essentially just creating a bunch of numbers under the screen, kind of like those same GPS coordinates. The dimension and the algorithm that you use to generate that vector can be different across different databases, but the actual meaning of that data will change. And so GoldenGate can replicate the vector data as long as the algorithm and the dimensions are the same. If the algorithm and the dimensions are not the same between the source and the target, then you'll actually want GoldenGate to replicate the base data that created that vector. And then once GoldenGate replicates the base data, it'll actually call the vector embedding technology to re-embed that data and produce that numerical formatting for you. 15:42 Lois: So, there are some nuances there… Nick: GoldenGate can also replicate and consolidate vector changes or even do the embedding API calls itself. This is really nice because it means that we can take changes from multiple systems and consolidate them into a single one. We can also do the reverse of that too. A lot of customers are still trying to find out which algorithms work best for them. How many dimensions? What's the optimal use? Well, you can now run those in different servers without impacting your actual AI system. Once you've identified which algorithm and dimension is going to be best for your data, you can then have GoldenGate replicate that into your production system and we'll start using that instead. So it's a nice way to switch algorithms without taking extensive downtime. 16:29 Nikita: What about in multicloud environments? Nick: GoldenGate can also do multicloud and N-way active-active Oracle replication between vectors. So if there's vectors in Oracle databases, in multiple clouds, or multiple on-premise databases, GoldenGate can synchronize them all up. And of course we can also stream changes from vector information, including text as well into different search engines. And that's where the integration with Elasticsearch and OpenSearch comes in. And then we can use things like NVIDIA and Cohere to actually do the AI on that data. 17:01 Lois: Using GoldenGate with AI in the database unlocks so many possibilities. Thanks for that detailed introduction to Oracle GoldenGate 23ai and its capabilities, Nick. Nikita: We’ve run out of time for today, but Nick will be back next week to talk about how GoldenGate has evolved over time and its latest features. And if you liked what you heard today, head over to mylearn.oracle.com and take a look at the Oracle GoldenGate 23ai Fundamentals course to learn more. Until next time, this is Nikita Abraham… Lois: And Lois Houston, signing off! 17:33 That’s all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We’d also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.
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