Your analytics team isn’t drowning in data — it’s drowning in a ticket queue. 300 requests a month, an 11-day median wait, and 68 percent of them are simple filters on data you already have. Bolting an LLM straight onto the warehouse doesn’t fix that — it just hallucinates joins and runs unreviewed SQL with a service account’s credentials. Databricks AI/BI Genie fixes it properly, if you curate and govern it instead of treating it like a chatbot demo.
Across 25 modules and 129 lessons you follow one data team — Meridian Retail, a 40-person analytics org buried under 300 tickets a month — as they replace the queue with governed conversational analytics. You stand up the meridian.retail Unity Catalog schema yourself, ship your first working Genie Space in twenty minutes, then go deep on the part most courses skip entirely: curation. You’ll engineer all four layers that separate a grounded, trustworthy agent from an expensive guessing machine — general instructions, example SQL queries, trusted assets (parameterized functions), and metric views — plus the Unity Catalog metadata hygiene (comments, constraints, tags, certification) that is, in effect, your prompt.
