MEASURE() queries you’d run in Databricks.
What Credible Reads
Your Unity Catalog metric views — YAML definitions with asource, dimensions, measures, joins, and an optional top-level filter. The agent works from that YAML. Connecting to the workspace is optional but the natural way to fetch and validate it: pull the definition with DESCRIBE TABLE EXTENDED <catalog.schema.view> AS JSON, discover views through Unity Catalog’s information_schema, or read through a Genie / AI-BI space where one is configured (measures are read only through the MEASURE() function).
What Comes Across
The everyday modeling carries over. Here’s how the bigger pieces land — and where they get better:The Migration Flow
Credible reads the metric-view YAML, translates the source, joins, dimensions, and measures to Malloy, enriches with#(doc)/#(index) tags, and — where the workspace is connected — validates row-by-row with SELECT <dim>, MEASURE(<measure>) … GROUP BY ALL.
What Credible Handles
MEASURE()-only access — metric-view measures can’t be read as plain columns, so Credible mirrors each aggregate expression, preserving semi-additive behavior instead of blindly re-aggregating.- Unity Catalog governance — grants, row filters, and column masks live in Unity Catalog and do not travel with the YAML. Credible re-establishes equivalent controls in its own access-control layer rather than pretending they came along.
- Upstream joins — because joining a metric view to other tables requires wrapping it in a CTE, those joins are modeled explicitly as Malloy sources and joins.
Databricks metric views are a recent Unity Catalog feature and the YAML spec is still evolving. Credible reads the current
version: from each definition; confirm the spec version in your workspace when you migrate.Before & After
A Unity Catalog metric view:filter becomes a source-level where:, aggregate expressions become measures (a SUM(CASE WHEN …) becomes a clean { where: … } filter), and format maps to render tags. Governance stays behind in Unity Catalog and is re-created in Credible.
More than a reformat. The model is AI-usable through the Context Engine and composes into follow-up questions your MEASURE()-bound metric views couldn’t express. See what you gain →
Next Steps
Migration Overview
The method behind every migration
Access Control
Re-establish Unity Catalog governance in Credible