databricks-metric-views

You can define governed business metrics for Unity Catalog by writing YAML definitions that separate measures from dimension groupings, enabling flexible querying and cross-team reuse across KPI dashboards and SQL queries.

Yes, YAML metric views support joining tables across star and snowflake schemas. This allows you to define complex aggregations and multi-dimensional analytics without altering the underlying schema structure.

YAML metric views require DBR 17.2 or higher with YAML v1.1 support. This environment ensures proper parsing and execution of dimensions, measures, joins, materialization, and window measures.

To create a minimal metric view, define a YAML file containing at least one dimension and one measure. This establishes a baseline reusable metric definition for immediate querying and cross-team reporting.

Yes, Unity Catalog supports pre-computed materialization patterns for metric views. This feature helps optimize query performance for complex aggregations and large-scale multi-dimensional analytics.

The best way to standardize metrics like revenue and orders is defining them as governed YAML metric views in Unity Catalog, separating measure logic from dimensions to ensure consistent cross-team reporting.