Your CEO just forwarded a conversational-analytics demo and said: “By Q3, anyone should ask our data in English and trust the answer.” That mandate lands on the data platform — not the app team. This course is that retrofit, built end to end on a real, messy, mid-migration warehouse.
You will follow Maya, Head of Data Platform at a $2B retailer, as she makes an inherited Teradata-to-Snowflake estate safe for AI agents to query in plain English. You start by measuring why naive LLM text-to-SQL fails — hallucinated joins, three definitions of “revenue”, confident wrong numbers — using a real evaluation harness. Then you fix it.
You design and author a semantic layer (metrics, dimensions, entities, a join graph) that grounds text-to-SQL accuracy from roughly 40% to roughly 90%. You choose a semantic-layer engine with a decision matrix — dbt Semantic Layer, Cube, Snowflake Semantic Views, Databricks Metric Views, LookML — and learn to defend the choice.
