If your role has felt quietly precarious these last three years, this essay explains why, and it earns the ten minute read. The claim: AI did not eat data engineering, it split it in two. It automated the mechanic and raised the price of the architect, upending two generations at once, the ones who learned the tools and the ones who learned the "why". Both now have to become something new. It is a longer, slower read, built to be sat with rather than skimmed. → check out this weeks edition of www.datapro.news Video edition ⬇️ Which side of it do you see in yourself, and what are you doing about it?

This week's newsletter digs into the real fork for anyone letting an AI agent near their pipelines, and it is not which model tops the benchmark. It is where the agent runs. Claude Code sits in your shell and harvests dbt metadata locally. OpenAI's Codex runs in a cloud container and needs your data uploaded before it can even start. Both are strong, both come with trade-offs. Which side are you on, and why? Have you let an agent run a dbt build unattended yet, or does that still make you nervous? Full breakdown in this week's newsletter → www.datapro.news · Video hot-take ⬇️

Being July 1, we're launching for the month a new weekly coding challenge designed specifically for Data Engineers! Follow the link in the Classroom below to see if you can master them, then post a screenshot here of your success (and remember, every time you post here, you get more points as well to level up on Skool! https://www.skool.com/data-innovators-exchange/classroom/c1ecc3fd?md=d46ce14f28f748ad8b4e8b487803f21e

https://handsondataeng.com/blog/claude-models-explained

If you're still designing your stack with a separate transactional layer, an analytical warehouse, and a real-time tier stitched together by pipelines — H1 2026 just made that architecture harder to justify. Every major platform shipped something this half that chips away at the need for that middle layer. This week's issue breaks down exactly what ➡️ Snowflake, ➡️ Databricks, ➡️ BigQuery, ➡️ Redshift, and ➡️ MS Fabric each did — and what the pattern means for the rest of the year. Video version of this weeks www.datapro.news below 👇