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
The old data industry is dying. The new one hasn't quite been born. And in between, we're seeing some strange things.
In this episode, I explore the Great Data Interregnum, a period when the playbook, practices, and architectures of yesterday are losing their coherence, but whatever replaces it remains uncertain.
In this episode of The Joe Reis Show, I sit down at my house in Salt Lake City with Kyle, founder of Credible Data.
Before founding Credible, Kyle spent nearly 17 years at Google building core distributed systems—including the in-memory shuffle/join backplane that powers BigQuery—before moving to Gradient Ventures and spending four years helping integrate Looker into GCP.
We dive deep into the reality of semantic layers, the origins of Malloy, and why the current hype around semantic layers often misses the mark. Kyle breaks down why SQL conflates data modeling and queries, how agents are shifting data work from painful bottom-up ETL to deterministic top-down domain modeling, and why giving an LLM an entire enterprise schema creates nothing but "data slop." We also get into the future of data engineering, what analysts should actually be learning today, and why unlocking the "long tail" of dark business data is where the true value lies.
Connect with Kyle & Credible: credibledata.com
Guest policy: The Joe Reis Show is strictly invite-only. Unsolicited guest pitches, PR agency solicitations, and booking requests are not accepted or reviewed.
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.
Guest policy: The Joe Reis Show is strictly invite-only. Unsolicited guest pitches, PR agency solicitations, and booking requests are not accepted or reviewed.
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
Guest policy: The Joe Reis Show is strictly invite-only. Unsolicited guest pitches, PR agency solicitations, and booking requests are not accepted or reviewed.
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.
Guest policy: The Joe Reis Show is strictly invite-only. Unsolicited guest pitches, PR agency solicitations, and booking requests are not accepted or reviewed.
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.
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.
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.
Guest policy: The Joe Reis Show is strictly invite-only. Unsolicited guest pitches, PR agency solicitations, and booking requests are not accepted or reviewed.
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.
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
Guest policy: The Joe Reis Show is strictly invite-only. Unsolicited guest pitches, PR agency solicitations, and booking requests are not accepted or reviewed.
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