Databricks

🎯 Intro Point an agent at a catalog with 400 tables and ask it a simple question. It reads schemas. Then samples rows. Then guesses at a join. Fourteen tool calls later it hands you a number that’s off by 12%, and you have no idea which step went wrong. Now hand the same agent three sentences: what a customer is, which table is the trusted one, and how the company counts “active.” One query. Correct answer. ...

🤔 The enterprise-wide enigma Ask three people on your data team what “total revenue” means and you’ll get three queries. One filters out returns. One rounds before summing. One groups by order date, another by ship date. All three are technically correct, and none of them match. This isn’t a data quality problem. The data is fine. The problem is that every dashboard, notebook, and BI tool re-implements the aggregation logic from scratch, and nothing stops those implementations from drifting apart from each other. ...