databricks-metric-views

You define governed business metrics in Databricks using YAML-based metric definitions that are applied to source tables as Unity Catalog metric views. This approach ensures consistent, reusable KPIs across teams and dashboards.

Databricks Unity Catalog metric views are standardized objects for defining, querying, and managing governed business metrics. You need them when building consistent KPIs across different dashboards, teams, and AI/BI tools.

Yes, you can define and maintain metric view definitions in YAML files. This allows you to version control and collaborate on standardized business metrics across your Databricks environments.

Yes, creating and managing metric views requires Databricks Runtime 17.2 or later. You also need appropriate permissions within your Databricks environment to apply these definitions to source tables.

You apply a YAML metric definition to a Databricks source table using the manage_metric_views action parameter. You specify the action as create, provide the target full_name, and set the source table.

Databricks metric views require Databricks Runtime 17.2+ and appropriate permissions. They are designed for standardized KPIs and revenue analytics, but environment compatibility is essential for seamless materialization.