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

Most of the work in building a Data Vault isn't the code. It's the design. Reading metadata, profiling samples, identifying business keys, capturing definitions nobody updates later. We filmed a walkthrough of the IRiS Assistant doing exactly that work. One source table, start to finish: profiled, modelled, definitions captured, production-ready metadata pushed to the repository, in a single conversation. If you've ever sat through the design phase of a Data Vault build, this is worth eleven minutes. The IRiS Assistant is live in beta: https://ignition-data.com/iris-assistant

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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 👇

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