In this episode, Matt Housley and I reunite for a Friday catch-up, bringing back some of that classic Monday Morning Data Chat energy. We dive into the absurdity of the "buzzword industrial complex," and why declaring it the "Year of Context" is mostly just industry hype, per usual.

We also tackle the chaotic reality of deploying AI agents (including the ultimate YOLO, OpenClaw) without proper data governance, the Anthropic class action lawsuit regarding copyright, and why regional conferences like DataTune are awesome. Finally, we discuss the shifting landscape of media, the death of traditional book publishing models, and the rise of the independent, niche creator.

The white-collar tech industry isn't what it used to be, and anyone could be on the chopping block at a moment's notice. With tens of thousands of highly skilled people getting laid off from Big Tech on a seemingly bi-weekly basis, competing in the traditional job market is brutal right now.In this episode, Jody Hesch and I discuss why building a freelance data consulting business isn't just a career pivot—it is a necessary Plan B. We break down the exhaustion of constantly reinventing yourself and navigating new team dynamics every time you switch full-time roles. We also explore the counterintuitive reality that by going freelance, you only have to build your network and reputation once to create a repeatable motion. Whether you are actively looking for an exit or just realizing that the gig economy is coming for data engineering, this conversation covers the realities of making the jump.

In this conversation, Paul Blankley and Ryan Janssen, founders of Zenlytic, drop in to discuss the massive shift in how we build software and handle data. We trace their journey from studying early NLP and Transformers at Harvard right when the BERT paper dropped, to building a company that relies on cutting-edge LLMs. As far as I know, they're the first to use LLM's for analytics.

We dive deep into the reality of the agentic era: engineers are no longer writing the bulk of the code; they are managing agents, verifying outputs, and maintaining ridiculously high standards. We also explore why the industry needs to embrace "net negative scaffolding" as models get smarter, and why having good "taste" might be the ultimate human moat left in tech.Bonus:

To prove that software development is changing faster than ever, we literally "vibe coded" a brand-new CRM called "Slop Force" in 20 minutes during this episode. Zenlytic: https://www.zenlytic.com/

We often hear about the AI skills gap, where people need to get training on the latest AI tools. There's also the AI competence gap, where people might not have the skills or competence in a field, and use AI to mask over those shortcomings. The results are what you expect - chaos. In this episode, I unpack these two gaps, and do my usual ranting about learning the fundamentals and investing in oneself.

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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 conversation, I sit down with Tim Delisle and Chris Crane, co-founders of 514, to discuss bridging the gap between software development and data engineering. We cover their experience leading global data engineering at Nike and why software teams are increasingly taking ownership of heavy analytical workloads.We also dive into how they are building the Moose Stack to give developers a local-first, code-first analytics experience. Finally, we explore how AI co-pilots are acting like an "army of interns" to fundamentally change how we write code , and why the "personal data lake" might be the future of privacy and local compute.Check out 514 & The Moose Stack: https://www.fiveonefour.com/

Sadie St. Lawrence joins me to unpack her concept of the "AI Orchestrator," explaining how it shifts our mindset from being a musician to a conductor in the age of AI. She shares insights from her work at the Human-Machine Collaboration Institute (HMCI), detailing how her team is building AI-powered solutions and tackling complex problems. We also chat about the common pitfalls in AI adoption, from unfounded fears to "work slop," and why foundational systems thinking remains paramount.

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

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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.