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
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.
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
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.
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...
In this episode of The Joe Reis Show, I catch up with my good friend Christophe Blefari, co-founder of NAO, to dig into what's actually happening on the front lines of AI agents and data engineering. We talk about the evolution from building SQL IDEs to agentic analytics harnesses, how prompt-driven workflows are reshaping data teams, and why age-old problems like data discovery, modeling, and documentation are suddenly roaring back. We also dive into the risks of "token slop," whether data and software engineering roles are destined to merge, Christophe’s take on DuckDB’s acquisition by AWS, and the realities of building AI tools across Europe and the US.Nao: https://getnao.io/
In this episode, I sit down with Sami Hero, CEO of Ellie.ai, to talk through the evolution of data modeling and why foundational conceptual modeling is more critical now than ever. With the explosion of AI agents, LLMs, and semantic layers, many teams assume they can bypass data modeling and just let an AI infer meaning directly from raw tables.
We dig into why that mindset leads to bad decisions, how companies end up with 15,000 Snowflake tables and thousands of unmaintained dashboards, and why defining core business concepts is the real prerequisite for reliable AI. We also discuss the resurgence of ontologies, lessons from old-school methodologies like MERISE, and how to balance speed with sound architecture.
AI agents are being pitched as the “death of the data team,” with CEOs supposedly able to do the work themselves. We’ve heard versions of this promise before, from self-service BI to low-code analytics.
In this episode, I revisit that history and ask what agents actually change. Who prepares the data, resolves conflicting definitions, and checks the answers? What does the hiring research tell us about demand for experienced people and opportunities for juniors?
In this episode of the Joe Reis Show, I sit down with David Aronchick, co-founder of Expanso and one of the early pioneers behind Kubernetes, Kubeflow, and the CNCF. We dive deep into the evolving data landscape and discuss why the architectural pendulum is swinging back from pure cloud centralization toward distributed edge compute. David breaks down the real operational nightmares of edge data collection, why treating your bronze tier as a "toxic waste dump" breaks downstream pipelines, and how applying schema and context upstream makes your data truly AI-ready. We also get into the critical need for data provenance, data bills of materials, and what agents actually need to communicate reliably.Website: https://expanso.io
I get asked all the time whether I’m still “hands-on.” The short answer: not in the traditional sense. My work has shifted toward writing, teaching, advising, investing, building companies, and working with the people building what comes next. In this episode, I argue that technical relevance isn’t about staying trapped in the same operating mode forever, and that AI is drastically changing what “hands-on” even means.
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The Joe Reis Show is invitation-only. Unsolicited guest pitches and PR outreach are not considered.
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