I’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.
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October 23, 202617 min
Scott’s Tots and the Broken Promise Economy
Ever feel like you've been lied to? In Scott’s Tots, Michael Scott promises a class of third graders he’ll pay their college tuition if they finish high school. Ten years later they’ve held up their end, and he shows up to tell them he's broke. A lot of people feel like those kids right now. They got the degree, learned to code, and followed the career plan they were handed, only to find a job market that feels empty and hollowed-out. In this episode I use Scott’s Tots to look at who made these promises, why most of them were sincere but wrong, and what the consolation prizes look like today.
October 20, 202651 min
"Blue Washed": The Rise and Fall of IBM Watson Health and Explorys w/ Doug Meil
In this episode, I sit down with Doug Meil, software veteran and author of The Rise and Fall of Explorys and IBM Watson Health. A decade ago, IBM Watson was the undeniable face of the modern AI revolution. Fresh off its historic Jeopardy win, IBM promised Watson would revolutionize entire industries and cure cancer. Instead, it became one of tech history’s most high-profile corporate implosions. Doug lived through the heart of it. His healthcare data startup, Explorys, was acquired in 2015 to serve as a foundational pillar for IBM Watson Health. What followed was a masterclass in what Doug calls the "paradox of inorganic growth"—an unravelling driven by corporate hubris, aggressive internal "blue washing," brutal cloud lock-ins, and leadership prioritizing marketing slogans over iterative product engineering. We dig deep into the untold engineering and product side of the acquisition, why enterprise mergers crush agile technical teams, and the enduring lessons today’s AI founders and leaders must take away before jumping headfirst into enterprise AI hype.Get Doug's book here: https://link.amazon/B05ATbGBL
October 16, 202619 min
Build a Career That Looks Like You
A lot of people ask me for career advice. My latest advice is learn the fundamentals, build useful things that interest you, and stop outsourcing your direction to everyone around you. But originality doesn’t automatically pay the bills, copying is part of learning, and “do what AI cannot” is lazy advice. In this episode, I unpack the advice, its limits, and how to find your own way without betting your livelihood on it. 🏂⛷️ Join us at Practical Data Summit — January 28–29, 2027, in Salt Lake City, Utah. We're bringing together around 300 of the brightest minds in data and AI for two days of candid conversations, practical talks, hands-on sessions, and meaningful connections. This isn't another massive vendor conference filled with sales pitches. We're building the kind of event we'd actually want to attend: a smaller, more intimate gathering of practitioners, builders, and leaders sharing what they've built, what worked, what didn't, and where the industry is heading. We'll have some incredible speakers, plenty of opportunities to meet people who are shaping the industry, and of course, the beautiful Wasatch Mountains right outside our door. Stick around for skiing, snowboarding, and après-ski fun over the weekend. Early bird tickets are $1,000 (regularly $1,300), and space is limited. Learn more and grab your spot at practicaldatasummit.com .
October 2, 202612 min
The Pendulums of Data
The data industry rarely moves in a straight line. We swing from centralized to decentralized, batch to real-time, general-purpose to specialized, abstraction to control, and governance to self-service. In this episode, I unpack why these cycles keep repeating, what forces push the pendulum in each direction, and why every successful architecture eventually creates the conditions for its own backlash.
September 25, 202616 min
This Year’s Better Mousetrap: New Tech, Same Old Problems
We see a lot of hype cycles in our industry - Data mesh. Generative AI. Agents. Now context. Every cycle brings a new label and a familiar promise: this will finally make your organization’s data useful. The technology has improved enormously. So why do the same problems keep coming back? In this episode, I explore how communication, incentives, and organizational politics shape what our tools actually deliver. Will context finally deliver the silver bullet to solve our problems? Is buying the next platform easier than resolving the messy organizational problems we all deal with? Listen to get my thoughts.
September 22, 202643 min
DuckDB - The Past, Present, and Future, DuckLabs, and More w/ Hannes Mühleisen
Hannes Mühleisen (co-creator of DuckDB) joins me to chat about the past, present, and future of DuckDB, Ducklabs (recently acquired by AWS), and more. Note - parts edited due to AWS's request.
September 21, 20261 hr 5 min
Why Off-The-Shelf AI Fails for Business Analytics w/ Rob Collie
In this episode, I'm joined by Rob Collie, CEO of P3 Adaptive, early pioneer of Power BI and Power Pivot at Microsoft, and author of the new book Fair Game: Customizing AI to Your Business is Easier Than You Think. Most business leaders play with off-the-shelf ChatGPT or Claude and think it's magic until it completely face-plants on their actual company data. Why? Because the LLM is just one Lego brick. Delivering real value requires the unsexy, "boring" work: structured context, reliable data modeling, and robust semantic layers.Rob and I dig into why 15 out of 16 people bounce off Excel, why semantic layers are hot, if dashboards are dead, and much more.
September 18, 20261 hr 0 min
Designing the AI-Driven Data Foundations: Architecture, Principles, and Practice w/ Sanjeev Mohan
Former Gartner VP Analyst and principal at SanjMo, Sanjeev Mohan, joins me to break down the reality of AI adoption, data foundations, and why enterprises are hitting roadblocks with agents and LLMs. From comparing the current AI boom to the dot-com era to unpacking his new concept, ECL (Extract, Context, Link), Sanjeev explains why moving data around is no longer viable and why "there is no AI stack, only one stack." We also dive into capability overhang, artificial guardrails, the race to standardize semantic context, and why writing books and understanding core primitives still matter in the age of generative models
September 18, 202616 min
Why Write Books?
This week at dbt Summit, Tristan Handy (co-founder of dbt) asked me why I write books. It's a good question, especially today when reading is declining and any answer is seemingly at your fingertips. In this Freestyle Friday, I discuss why I write books, why they still matter, and much more. If you're interested in writing a book, this episode is definitely a must-listen.
September 11, 202616 min
Nobody Knows...
I talk to executives and practitioners around the world about AI and what comes next. Everyone has predictions and pontifications, and everyone seems to think someone else has the answers. But the more conversations I have, the clearer it gets that nobody knows what's next. In this episode, I explore that uncertainty and how we move forward anyway. Because in the end, nobody knows...
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