DOP 337: Nanoseconds Matter - InfluxDB and the Future of Real-Time Data
Release Date: 02/11/2026
DOP 339: DNS Is Old Tech (And That's Why It Still Runs the Internet)
DevOps Paradox
#339: DNS has been around since the 1980s. Nobody's writing blog posts about how it changed their life. But every single thing on the internet depends on it -- including all those AI tools everyone's excited about. Anthony Eden has been in the DNS business since the late nineties, when he was CTO of one of the first seven domain registrars after the .com deregulation. In 2010 he started DNSimple, and he did it without a dime of venture capital. Sixteen years later, his 20-person team runs a global DNS infrastructure with 14 edge nodes and 9 origin servers spread across multiple continents. The...
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DOP 338: The Assembly Line Problem: Why Adding AI to One Step Breaks Everything
DevOps Paradox
#338: Every company adding AI coding tools runs into the same wall. Developers produce more code, but features don't ship any faster. The bottleneck just slides downstream -- to QA, to security, to legal, to whoever comes next in the pipeline. And the team that got faster? They don't even realize the people upstream could be feeding them more work. Viktor's take: the fastest possible setup is one person carrying a feature from idea to production. Not one person doing everything alone -- a system designed so nobody waits. Tests run in CI. Deployments happen through Argo CD. Security scanning is...
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DOP 337: Nanoseconds Matter - InfluxDB and the Future of Real-Time Data
DevOps Paradox
#337: Time series databases have become essential infrastructure for the physical AI revolution. As automation extends into manufacturing, autonomous vehicles, and robotics, the demand for high-resolution, low-latency data has shifted from milliseconds to nanoseconds. The difference between a general-purpose database and a specialized time series solution is the difference between a minivan and an F1 car - both will get around the track, but only one is built for the demands of real-time operational workloads. The open source business model continues to evolve in unexpected ways. While...
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DOP 336: Why Top Talent Won't Work for You Anymore
DevOps Paradox
#336: The workplace is on the verge of a transformation as significant as the Industrial Revolution. Just as Bring Your Own Device policies emerged after the iPhone disrupted corporate mobile standards, we are now entering an era where employees may arrive with their own AI teams in tow. The question is no longer whether AI will change hiring and employment - it is how quickly companies will adapt before being left behind by competitors who embrace this shift. Current AI productivity gains remain largely individual rather than organizational. Writing code twice as fast means nothing if the...
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DOP 335: Stop Building Dashboards and Start Getting Answers With Coroot
DevOps Paradox
#335: Observability tools have exploded in recent years, but most come with a familiar tradeoff: either pay steep cloud vendor markups or spend weeks building custom dashboards from scratch. Coroot takes a different path as a self-hosted, open source observability platform that prioritizes simplicity over flexibility. Using eBPF technology, Coroot automatically instruments applications without requiring code changes or complex configuration, delivering what co-founder Peter Zaitsev calls opinionated observability—a philosophy of less is more that aims to reduce cognitive overload rather than...
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DOP 334: If Code Is the Easy Part, What Should Developers Actually Be Doing?
DevOps Paradox
#334: The debate over whether AI saves developers time misses a fundamental truth: coding was never the hardest part of software development. Writing code is mechanical work - the real challenges have always been understanding problems, designing solutions, communicating with stakeholders, and navigating organizational complexity. AI is now forcing a reckoning with this reality, pushing developers at every level to reconsider what skills actually matter. The traditional separation between architects who design and developers who implement is breaking down. AI enables a return to something like...
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DOP 333: The Hidden Problems Behind Every Data Pipeline
DevOps Paradox
#333: Pete Hunt, CEO of Dagster and early React team member, explores the evolution from Facebook's early React development through trust and safety infrastructure at Twitter, to building modern data orchestration tools. The conversation reveals how similar infrastructure problems plague every industry - whether you're launching rockets or managing porta-potties, the core challenges remain consistent: late data, quality issues, and mysterious errors that require both automated solutions and human oversight. The discussion dives into the technical realities of scaling systems, from the...
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DOP 332: 2026 - The Year of Discovery
DevOps Paradox
#332: AI adoption in enterprise software development is accelerating, but operations teams are lagging behind. While application developers embrace AI tools at a rapid pace, those on the ops side remain skeptical—citing concerns about determinism, control, and a general resistance to change. This mirrors previous technology waves like containers, cloud, and Kubernetes, where certain groups initially pushed back before eventually adapting. The prediction for 2026: AI will not see widespread adoption in operations despite its growing presence elsewhere in the software lifecycle. The bigger...
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DOP 331: Looking Back on Our 2025 Predictions
DevOps Paradox
#331: At the end of 2024, predictions were made about what 2025 would bring to the tech industry. A year later, on New Year's Eve, it's time to look back and see what actually happened. The prediction episode from January 1st covered four major topics: rug pulls from companies switching to business source licenses, the rise of WebAssembly adoption, a wave of company acquisitions, and AI becoming embedded in existing tools. Some predictions hit the mark while others missed entirely, but what emerged was something nobody fully anticipated. YouTube channel: Review the podcast on...
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DOP 330: Merry Christmas (You Should Probably Be Doing Something Else)
DevOps Paradox
#330: In this short episode, Darin and Viktor reflect on the holiday season. YouTube channel: Review the podcast on Apple Podcasts: Slack: Connect with us at:
info_outline#337: Time series databases have become essential infrastructure for the physical AI revolution. As automation extends into manufacturing, autonomous vehicles, and robotics, the demand for high-resolution, low-latency data has shifted from milliseconds to nanoseconds. The difference between a general-purpose database and a specialized time series solution is the difference between a minivan and an F1 car - both will get around the track, but only one is built for the demands of real-time operational workloads.
The open source business model continues to evolve in unexpected ways. While companies like Elastic and Redis have seen hyperscalers fork their projects, a new partnership paradigm is emerging. Amazon Web Services now pays to license InfluxDB and offers it as a managed service, signaling a shift toward collaboration rather than competition. This approach benefits everyone: vendors maintain development velocity, cloud providers get workloads on their platforms, and customers receive better-supported products.
Evan Kaplan, CEO of InfluxData, joins Darin and Viktor to discuss the trajectory from observability metrics to physical world instrumentation, why deterministic models matter more than probabilistic ones when your robot might run over your cat, and what it takes to build a sustainable open source company over a decade-plus journey.
Evan's contact information:
LinkedIn: https://www.linkedin.com/in/kaplanevan/
YouTube channel:
https://youtube.com/devopsparadox
Review the podcast on Apple Podcasts:
https://www.devopsparadox.com/review-podcast/
Slack:
https://www.devopsparadox.com/slack/
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