databricks-metric-views

You define reusable business metrics in Unity Catalog by writing a YAML definition that specifies source tables, dimensions, measures, and optional joins to standardize KPIs across dashboards. This approach ensures consistent metric definitions for cross-team reporting.

Standardizing KPI definitions across multiple dashboards in Databricks is best achieved by defining governed metrics in YAML and storing them as metric views in Unity Catalog. This allows teams to share a single source of truth for revenue and conversion metrics.

Yes, you can model star or snowflake schema joins declaratively in YAML for Databricks metric views. The YAML body supports specifying source tables, dimensions, measures, and optional joins to structure your data model.

Yes, Unity Catalog is required to use governed metrics defined in YAML. This skill specifically leverages Unity Catalog to store and manage standardized business metrics, ensuring consistent definitions across tools and cross-team dashboards.

You query on-demand metrics defined in YAML by using the MEASURE() function in SQL. This allows flexible querying of the standardized business metrics stored as metric views in Unity Catalog.

A metric view in Databricks is a governance-ready, reusable business metric definition stored in Unity Catalog using YAML. You need one when building standardized KPIs that require consistent metric definitions across multiple tools and cross-team dashboards.