Claim OwnershipThe Joe Reis Show
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What happens when a best-selling author and "recovering data scientist" gets a microphone? This podcast.
I'm Joe Reis, and each week I broadcast from wherever I am in the world, sharing candid thoughts on the data, tech, and AI industry.
Sometimes it's a solo rant. Other times, I'm chatting with the smartest people I know.
If you're looking for an unfiltered perspective on the state of AI, data, and tech, you've found it.
I'm Joe Reis, and each week I broadcast from wherever I am in the world, sharing candid thoughts on the data, tech, and AI industry.
Sometimes it's a solo rant. Other times, I'm chatting with the smartest people I know.
If you're looking for an unfiltered perspective on the state of AI, data, and tech, you've found it.
350 Episodes
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This week, I published an article called "2028, the Great Data Reckoning," which got a ton of response. Although I originally meant it to be satire, when I re-read it I felt like it was actually a glimpse into what's happening in our field right now. In this episode, I chat about the implications of the Great Data Reckoning on practitioners, leaders, and founders. Article: https://joereis.substack.com/p/2028-the-great-data-reckoning----------🚨Also, if you happen to be in San Francisco on March 26th, please join me at Undercurrent, a small and tech-focused conference for data engineers and architects. No sponsors, no salespeople, no bullshit. Just great technical discussions all day.Register here: https://cnfl.io/3ZCTaVx
In this episode, I sit down with Prashant Sridharan, a 30-year veteran of developer marketing who has shaped go-to-market strategies for tech giants like Sun Microsystems, Microsoft, AWS, Facebook, and Twitter, and currently runs product marketing at Supabase. We dive deep into the origins of DevRel and how marketing to developers has evolved in an increasingly noisy, AI-saturated landscape.Topics covered:- Transitioning from massive tech companies to the fast-paced startup world - How to genuinely measure the success of Developer Relations without ruining communities - Using AI tools like Claude to accelerate mechanical marketing tasks while preserving authentic storytelling - The shift from traditional SEO to GEO (Generative Engine Optimization) for developer tools - The thrill of live, unscripted coding demos and stories from sharing the stage with Steve Ballmer - Prashant's upcoming fiction novel, The Midnight Coders Children, and the craft of writing Find more from Prashant at StrategicNerds.com and check out his non-fiction book, Picks and Shovels: https://amzn.to/4cJ2TRO
For 40+ years, the data industry has tried to teach good practices and get adoption, often in the same way. And for 40+ years, that approach keeps failing over and over. Based on the recent Practical Data Community Survey, practitioners face challenges like time pressures, lack of direction, and lack of clear ownership. Do we need to try something else as an industry? Or do we continue to be the poster child for the definition of insanity - doing the same thing over and over, yet expecting different results? I hope not.
Why are we still using row-based protocols like ODBC and JDBC in a column-oriented world? In this episode, I sit down with Ian Cook, co-founder of Columnar and a long-time Apache Arrow contributor, to discuss the critical infrastructure changes needed to speed up modern analytics and AI.We dive deep into the technical bottlenecks of legacy standards - specifically the "serialization tax" of converting columns to rows and back again - and how ADBC (Arrow Database Connectivity) solves this by keeping data columnar from end-to-end. Ian also shares his insights on the intersection of tabular data and LLMs, why AI agents need better access to OLAP systems, and the tension between vibe coding speed and the stability required for critical open-source infrastructure.
The 2026 Practical Data Community State of Data Engineering dropped this week. It's full of some obvious and very counterintuitive information about the state of data engineers around the globe, in all sizes and types of organizations. Check it out!Also, I talk about the book writing process, where I messed up on this latest book, it's progress toward publication, and more.Survey: https://joereis.github.io/practical_data_data_eng_survey---------------------This episode is brought to you by Ellie.aiEllie makes data modeling as easy as sketching on a whiteboard—so even business stakeholders can contribute effortlessly. By skipping redraws, rework, and forgotten context, and by keeping all dependencies in sync, teams report saving up to 78% of modeling time.Check out Ellie: https://ellie.ai/
I sat down with Paul Dudley (CEO) and Ricky Thomas (CTO) from StreamKap to catch up on where the world of streaming data is heading—and things have changed fast since we last spoke.We dive into the concept of "vibe coding" and how AI is radically accelerating how we build software (I even share a story about building a data analysis tool in an hour). But the real meat of this conversation is about the intersection of streaming data and AI agents. Everyone is building agents, but without real-time context, they’re flying blind. We discuss why streaming is a missing link for agentic workflows, the shift from dashboards to automated decision-making, and why SaaS companies are racing to build walled gardens around their data.We also get into the nitty-gritty of the UK vs. US tech markets, the resurgence of PR in the AI era, and StreamKap’s upcoming move into the Snowflake native app ecosystem.Streamkap: https://streamkap.com/
This week was a doozy with new AI releases, the stock market, and more. It really feels like this was the first tremor in AI's impact on the SaaS market. What's do I think is next? Listen and find out.
In this episode, I sit down with Mike Driscoll, founder of Rill Data, to discuss the evolving landscape of business intelligence and data engineering. We explore why the industry keeps "rediscovering" old concepts like the semantic layer and how the rise of AI agents is forcing us to rethink how we structure data.Mike shares his insights on the "shape" of analytics, debating whether conversational interfaces will replace dashboards or simply complement them. We also dig into the growing demand for data engineering, the importance of watermarks and temporal semantics, and why data visualization remains a critical tool for "trust but verify" in an AI world.Rill Data Mike’s Podcast: Data Talks on the Rocks
As I use AI, I'm finding that I create MORE work for myself, not less. One task completed means five more to do. This is the paradox of today - AI might actually mean more work, not less. I talk about this, the Data Day Texas final episode, and more.Check out the review I did of Cube's new analytics agent: https://www.youtube.com/watch?v=p3frGJOUl1E(Thanks to Cube for partnering on the review)
Lak Lakshmanan had a successful career in Private Equity and Big Tech, but he realized he couldn't just "coach the game" while the rules were changing. He had to get back on the field play it. We discuss vertical AI, the "foolhardiness" required to start a company , the reality of the AI technology wave, and why sitting on the sidelines is the biggest risk of all.LinkedIn: https://www.linkedin.com/in/valliappalakshmananGenerative AI Design Patterns (book): https://amzn.to/45v0xBO
In this episode, I talk about how I'm kind of living in a bubble of cool tech and AI, and how the 99% of businesses out there are still grappling with the same old data and tech problems they've always dealt with.I also talk about how me and my friends are using AI to automate the boring stuff and scratch our own itches.
In this episode, I sit down with science fiction author, activist, and journalist Cory Doctorow to unpack his viral concept of Enshitification, the three-act tragedy of platform decay: 1. be good to users 2. lock them in 3. extract value from users to feed advertisers and shareholdersWe also dive into:- The AI bubble: Cory’s case that parts of the sector are propped up by aggressive accounting and incentives, not durable value.- The “Reverse Centaur”: How workers (from Amazon drivers to radiologists) are being reorganized to serve machine workflows, rather than machines serving humans.- Software engineering vs. “vibe coding”: Why autocomplete isn’t engineering, and why AI can’t replace process knowledge and domain context.- The Post-American Internet: What happens when the U.S. weaponizes platforms, and the rest of the world builds alternatives.About Cory Doctorow: Cory is a multi-time international bestselling author, special advisor to the Electronic Frontier Foundation, and creator of the blog/newsletter Pluralistic.If you got value from this conversation, hit Follow and share it with one person who cares about the future of tech.
Tech is full of smart people with smart ideas - enterprise data models, ontologies, data mesh, proprietary AI strategies - that repeatedly fail to gain traction. When they fail, the blame usually goes to "stupid users", "lazy and immature organizations." Perhaps, but I don't think that's the whole story, and if you adopt that mindset, you're sure to keep failing.I think there's more to the story. Listen and find out...
In this episode, I visited the Hex office and sat down with Barry McCardle (CEO of Hex) to talk about the massive shift we’re seeing in the data stack. Countless companies have spent decades buying BI tools in the hope of "self-serve Nirvana," yet most dashboards still raise more questions than they answer. Barry and I dive into why the traditional dashboard is becoming a "jumping-off point" rather than a destination, and how AI agents are finally closing the gap between having a question and getting a sophisticated answer.We also discuss building tools people love, "commitment engineering", Barry's story, and much more.
What status game are you playing? Are you trying to outcompete others, or playing your own game? In this episode, I talk about status games in data and careers in general.
The technology industry is prone to moving fast and forgetting its history. This is a shame because our industry is built on the shoulders of many giants, often long forgotten. Bill Inmon, Roger Whatley, and I discuss the history of technology and computing, covered in their new book, From Stone to Silicon. We talk about the big people and moments in technology and computing, and much more.From Stone to Silicon (book): https://amzn.to/4pLfqat
Welcome to 2026! In this spontaneous Friday AMA, I take listener questions on ontologies, the “leaky abstractions” of AI coding tools, why the “button pusher” era of engineering is a professional dead end, and the shifting landscape of data engineering.I also provides an update on my upcoming book, Mixed Model Arts (launching in March 2026), and discuss the unexpected convergence of library science, ontologies, and traditional data modeling, something not on my 2025 bingo card.Great turnout, especially for no notice. Thanks to everyone who showed up!
Happy 2026! In this episode, I rant about whether vibe coding and AI coding agents makes the Law of Leaky Abstractions obsolete, making your first dollar (or whatever currency), and more.The Law of Leaky Abstractions: https://www.joelonsoftware.com/2002/11/11/the-law-of-leaky-abstractions/If you like this podcast, please take 10 seconds and give it a rating or review on your podcast platform of choice. It will go a long way to giving the show more visibility. Thanks!
2025 is nearly gone, and in this episode, I give some thoughts on what I think might happen in 2026. I also chat about this week's surge of interest in Kimball vs Inmon (and the podcast I tried to organize with them) and much more.
“What I built today might be obsolete tomorrow.”This is something I heard this week from a developer, and this is not uncommon given the warp speed nonstop advancement of AI models every week. We used to measure the rate of change in months or years. Now it’s days or weeks.In this episode, I talk about why writing code is rarely hard part, and why having good taste and shipping things that people love is the most important things we can do.
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