databricks-metric-views

You can define reusable business metrics in Databricks by creating YAML metric views that specify version, source, dimensions, and measures, then querying them with the MEASURE() function in SQL to standardize KPI calculations.

Unity Catalog metric views standardize KPI calculations by centralizing metric definitions in YAML, separating measures from dimension groupings to ensure consistent query-time aggregation across dashboards, Genie, and SQL queries.

Yes, Databricks metric views support complex modeling patterns like joins, materialization, and window measures, allowing you to model star or snowflake schemas and perform time-based calculations within your YAML definitions.

The best way to govern business metrics in Databricks is to centralize their definitions using Unity Catalog metric views, which enforces a YAML schema for dimensions and measures to ensure safe, consistent query-time aggregation.

Yes, you need Unity Catalog, as the YAML-driven metric views leverage Unity Catalog to define and govern reusable business metrics, applying the schema to standardize calculations and enable safe query-time aggregation.

Creating a YAML metric view in Databricks requires a schema with version, source, dimensions, and measures, while also supporting optional configurations for joins and materialization to handle complex data modeling.