Metric Views in Databricks: The Missing Layer for AI Agents

AI is only as good as the layer underneath it. That layer in Databricks is called a Metric View: a semantic model that defines your measures, your joins, and the rules for how data fits together.

In this episode, Stefan walks through the full stack end-to-end on a real LEGO dataset: building Metric Views in Databricks, generating them with dbt, deploying through Databricks Asset Bundles, and finally exposing it all through a Genie Space that business users can chat with. Along the way we get into some deeper questions: what the limits of metric views look like, how access control works, what BI "shifting left" actually means for engineering teams, and how you organize this in data products.

If you saw our Snowflake Intelligence episode, this is the Databricks counterpart. The Snowflake episode can be found here: www.youtube.com/watch?v=Gp-BntPgpcU