The Joe Reis Show

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:
Website: credibledata.com

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.

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

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.

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.