Stay ConnectedI’m Joe Reis, co-author of Fundamentals of Data Engineering, creator of Mixed Model Arts, international speaker, and long-time observer of the tech industry’s favorite recurring mistakes.
Weekly broadcasts from somewhere on the road. Expect raw solo rants and long-form conversations with the engineers and leaders actually building things. I dive into data, AI, architecture, and career survival.
This podcast is invitation-only. Unsolicited guest pitches and PR outreach are not considered.
Copyright: © Joe Reis
Matt Turck (VC at FirstMark) joins the show to break down the most controversial MAD (Machine Learning, AI, and Data) Landscape yet. This year, the team "declared bankruptcy" and cut over 1,000 logos to better reflect the market reality: a "Cambrian explosion" of AI companies and a fierce "struggle and tension between the very large companies and the startups".
Matt discusses why incumbents are "absolutely not lazy" , which categories have "largely just gone away" (like Customer Data Platforms and Reverse ETL) , and what new categories (like AI Agents and Local AI) are emerging. We also cover his investment thesis in a world dominated by foundation models, the "very underestimated" European AI scene , and whether an AI could win a Nobel Prize by 2027.
https://www.mattturck.com/mad2025
I travel a TON, and the most frequent questions I get relate to traveling: Why I do it and any tips I have for traveling.
Here, I answer those questions and more.
Jeremiah Lowin, founder of Prefect , returns to the show to discuss the seismic shift in the data and AI landscape since our last conversation a few years ago. He shares the wild origin story of FastMCP, a project he started to create a more "Pythonic" wrapper for Anthropic's Model Context Protocol (MCP).
Jeremiah explains how this side project was incorporated into Anthropic's official SDK and then exploded to over a million downloads a day after MCP gained support from OpenAI and Google.
He clarifies why this is an complementary expansion for Prefect, not a pivot , and provides a simple analogy for MCP as the "USB-C for AI agents".
Most surprisingly, Jeremiah reveals that the primary adoption of MCP isn't for external products, but internally by data teams who are using it to finally fulfill the promise of the self-serve semantic layer and create a governable, "LLM-free zone" for AI tools.
I'm back, and give some notes from the road, thoughts on choosing tools and vendors, having a plan B for tools, and more.
There's no shortage of technical content for data engineers, but a massive gap exists when it comes to the non-technical skills required to advance beyond a senior role. I sit down with Yordan Ivanov, Head of Data Engineering and writer of "Data Gibberish," to talk about this disconnect.
We dive into his personal journey of failing as a manager the first time, learning the crucial "people" skills, and his current mission to help data engineers learn how to speak the language of business.
Key areas we explore:
The world of data is being reset by AI, and the infrastructure needs to evolve with it. I sit down with streaming legend Tyler Akidau to discuss how the principles of stream processing are forming the foundation for the next generation of "agentic AI" systems.
Tyler, who was an AI cynic until recently, explains why he's now convinced that AI agents will fundamentally change how businesses operate and what problems we need to solve to deploy them safely.
Key topics we explore:
I still see some companies acting sheepish with AI, too scared to even try it out. That's a massive mistake. Now is the time to play offense with incorporating AI into your company and reimagining what it can become.
Are dashboards dead? For complex enterprise use cases, the answer might be yes. In this episode, I'm joined by Irina Malkova (VP Data & AI at Salesforce), to discuss her team's transformational journey from building complex dashboards to deploying AI-powered conversational agents.
We dive deep into how this shift is not just a change in tooling, but a fundamental change in how users access insights and how data teams measure their impact.
Join us as we cover:
It's all about acquisitions, acquisitions, acquisitions! Matt Housley joins me to tackle the biggest rumor in the data world this week: the potential acquisition of dbt Labs by Fivetran. This news sparks a wide-ranging discussion on the inevitable consolidation of the Modern Data Stack, a trend we predicted as the era of zero-interest-rate policy ended.
We also talk about financial pressures, vendor exposure to the rise of AI, the future of data tooling, and more.
In this episode, I sit down with Saket Saurabh (CEO of Nexla) to discuss the fundamental shift happening in the AI landscape. The conversation is moving beyond the race to build the biggest foundational models and towards a new battleground: context. We explore what it means to be a "model company" versus a "context company" and how this changes everything for data strategy and enterprise AI.
Join us as we cover:
Model vs. Context Companies: The emerging divide between companies building models (like OpenAI) and those whose advantage lies in their unique data and integrations.
The Limits of Current Models: Why we might be hitting an asymptote with the current transformer architecture for solving complex, reliable business processes.
"Context Engineering": What this term really means, from RAG to stitching together tools, data, and memory to feed AI systems.
The Resurgence of Knowledge Graphs: Why graph databases are becoming critical for providing deterministic, reliable information to probabilistic AI models, moving beyond simple vector similarity.
AI's Impact on Tooling: How tools like Lovable and Cursor are changing workflows for prototyping and coding, and the risk of creating the "-10x engineer."
The Future of Data Engineering: How the field is expanding as AI becomes the primary consumer of data, requiring a new focus on architecture, semantics, and managing complexity at scale.
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