databricks-metric-views

You define governed business metrics in Databricks Unity Catalog by creating metric views that store reusable KPIs with customizable dimensions and aggregates, ensuring consistent definitions across teams. YAML-based definitions are supported for standardized metric creation.

Yes, you can query Databricks metric views using SQL-like syntax to retrieve measures grouped by specific dimensions directly. This unified querying capability allows you to fetch aggregated business metrics without writing complex underlying SQL queries.

Creating metric views in Databricks requires a configured Unity Catalog environment and the Python pandas library. You need existing source tables in your catalog schema to serve as the foundational data for defining your business metrics.

You manage permissions for business metrics in Unity Catalog by granting privileges directly on the metric views. This governance feature ensures compliance and team collaboration by controlling access to centralized, reusable metric definitions.

Yes, this approach supports YAML-based metric definitions for Databricks to standardize KPIs, revenue metrics, and order analytics. Using YAML ensures consistent definitions of business metrics that require governance and reusability across different tools.