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

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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/valliappalakshmanan

Generative 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 shareholders

We 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.

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