The Joe Reis Show

In this episode, Tristan Handy and I sit down to unpack a massive shift coming to the data industry: over the next 12 months, the primary consumers of data won't be humans. They will be AI agents. We dive deep into what this means for data infrastructure, compute costs, and the tools we use every day. We also talk about processing high-volume agent queries, building "context stores", and why the industry shouldn't just build "horses with wheels" when designing agentic data engineers. We also take a fun detour comparing the current AI landscape to the early days of dial-up modems and Mosaic browsers , and discuss why stepping away from the screen and going old-school might be the ultimate productivity hack.

NOTE - Sorry for the edits in this video. I used Descript to edit out the umms and uhhs, and it was a bit too aggressive. Will make it less jarring in future videos. Thanks.Freestyle Friday, May 15, 2026Walking around Salt Lake City and unpacking the April 2026 data modeling survey results (334 respondents). Across three surveys now: January's State of Data Engineering (1,100), March's AI usage poll (193), and April's data modeling deep-dive. Not surprisingly, the same two pain points keep surfacing: time pressure and lack of clear ownership.90% of respondents have a data modeling pain point. When asked what would actually help, only 4.8% wanted better tools. Training, business requirements, time, and ownership crushed tooling in the rankings. Will AI improve things or make them worse? Time will tell...Also covered:Why physical data modeling has become the default (and why that's a problem)Data modeling vs. schema design - they're not the same thingSemantic layers (yay or nay?), Lloyd Tabb, and MalloyConway's Law, Reis's Law, and what changes when org charts get flattened by AIWhy leadership is under more pressure than everThe June half-year survey is coming🎙️ SPONSORSFivetran - stop cobbling pipelines together. Set it, forget it, scale as you grow.→ https://fivetran.comRevify - surprise Snowflake bills? One customer cut theirs 50% in 48 hours.→ https://revify.com/demo

Are AI agents silently draining your cloud data budget? With the rise of consumption-based pricing and autonomous AI queries, data teams are facing a perfect storm of skyrocketing costs and operational chaos. In this episode, I sit down with Sanjay Agrawal, CEO and Co-founder of Revefi, to discuss the intersection of data engineering, cloud warehouse optimization, and FinOps in the age of AI.We chat about how legacy on-prem habits are bankrupting modern data platforms, why query optimization is more about ROI than just speed, and how AI agents are changing the landscape of data consumption. Sanjay shares his deep expertise from building world-class databases at Microsoft and ThoughtSpot, revealing how to automate cost management and performance tuning for Snowflake, Databricks, and BigQuery.Key Topics:The evolution of cloud data warehouse pricing and why it breaks traditional budgets.How AI agents are causing massive, unpredictable spikes in compute spend.Real-world horror stories of ""lift and shift"" cloud migrations.Why database benchmarks focus on speed but ignore the actual ROI of data.The future of open table formats (Iceberg) and multi-engine routing.

Zach Wilson and I happen to be in Stockholm, Sweden, this evening. In this Freestyle Friday chat, we talk about what it takes to be a data engineer in 2026 and much more.

Josh Wills has spent 25 years writing data pipelines, with a career spanning Cloudera, as Director of Data Engineering at Slack, on the dbt DuckDB adapter, and now training foundation models at Datology AI. He uses coding agents every day. And he keeps running into the same wall: the agents jump to conclusions, fix the wrong thing, and ship pipelines no one understands.In this conversation, we unpack why AI agents struggle with the messiest, highest-stakes parts of data work, and what it means for the engineers managing them.We get into:- Big Data is back- Why AI agents jump to conclusions on benchmarks and complex bottlenecks- The $200K vibe-coded pipeline problem nobody wants to talk about- Why there's no training data for the gnarly enterprise pipelines that actually power businesses- "We're all managers now" - managing unreliable agents like managing unreliable people- Wicked problems and the limits of intelligence- Why politics is the last human endeavor to fall to LLMs (the data is never written down)- Whether classical ML still has a place (yes)- What Josh would tell a new grad starting in data today