Databricks adds Unity Catalog metric views to Power BI in private preview

Databricks added private-preview support for publishing Unity Catalog metric views into Microsoft Power BI semantic models. Teams can keep measures and dimensions defined in Unity Catalog and make them available in Power BI without rebuilding those definitions there.

Publishing turns a metric view into a table in the Power BI semantic model. Dimensions become columns, and eligible measures become explicit DAX calculated measures. Power BI queries the metric view rather than its underlying tables. Dimensions from joined tables appear in the same Power BI table without Power BI recreating those relationships.

DirectQuery is the default storage mode. Visual queries run through Databricks SQL to the metric view, so Unity Catalog access controls and security policies apply. Import mode is also supported, but it copies data into Power BI. That copy is governed by Power BI permissions rather than Unity Catalog controls.

Users can publish once from Catalog Explorer or add a Power BI task to an Azure Databricks job to republish on a schedule. Each job run publishes or refreshes the semantic model in the selected Power BI workspace. Republishing updates existing DAX measures, adds newly declared measures and dimensions, and retains existing fields. The broader Power BI task feature is in public preview, separate from the private preview for metric-view publishing.

Publication runs in one direction. Changes to a published measure in Power BI are not written back to Unity Catalog. Supported changes to the metric view can flow into Power BI when the model is republished.

The preview also comes with model constraints. A publishable metric view must have at least one dimension and no more than one fact source. Measures can be dropped when their source columns are not declared as named dimensions. The documentation also lists unsupported constructs—including leading window measures, percentiles, parameters, wildcard measures and complex-type dimensions—that are dropped during publication. Publishing multiple metric views together can fail if they define the same measure name differently.

Access requires an Azure Databricks workspace enrolled in the private preview, followed by an administrator enabling the Publish Metric Views To Power BI preview. The available documentation does not specify pricing, a general-availability date, named preview customers or equivalent support for deployments on other clouds.