databricks-metric-views

To define reusable business metrics in YAML, you specify dimensions and measures within a Unity Catalog metric view, separating metric definitions from queries to ensure consistency across Databricks dashboards and SQL workflows.

Yes, you can build governed metrics using star or snowflake schema joins. The metric view definitions support optional joins to integrate data across complex schemas for flexible analytics modeling.

The best way to standardize revenue metrics across cross-team dashboards is to define them as governed metric views in YAML, enabling consistent application and centralized governance across teams.

Yes, Databricks metric views support complex aggregations and window measures. You can define these advanced analytical calculations directly in YAML alongside your dimensions for materialization.

Yes, you need Databricks Runtime 17.2 or higher to use YAML metric views. This environment requirement ensures full support for defining dimensions, measures, optional joins, and materialization.

After creating a YAML metric view by defining dimensions and measures, you query the defined metrics using the MEASURE() function across your specified SQL workflows and dashboards.