Master once. Activate everywhere.
Govern Databricks metric views alongside every other semantic model. One mastered source. Governance in the middle.
Semantic models are created everywhere, mastered nowhere
Databricks defines metric views. Power BI, Looker, and Snowflake each define their own semantics too. Every platform governs its own definitions. Nobody governs all of them. The result: conflicting definitions, no single owner, and AI features like Genie that inherit the gaps. The problem is structural: semantics are created where data lives, mastered nowhere.
How Semantic Model Mastering Works with Databricks
Four capabilities that take your Databricks metric views from isolated definitions to a single, governed source of truth — mastered at the point of consumption through data products.
Import Databricks metric views
Upload metric view YAML definitions from Databricks to create governed data products, alongside semantic models from Snowflake, Power BI, Looker, and Cube. Built on the Open Semantic Interchange standard for cross-platform portability.
Govern through data products
Promote semantic models to data products with ownership assignment, approval workflows, version control, and quality standards. The data product becomes the mastered version, governed in one place.
Enrich beyond any single platform
Add glossary terms, curated descriptions, steward annotations, and relationship mappings. Richer context flows to downstream AI features, improving agent performance without changing how teams work.
Bring mastered semantics back to Databricks
Governed, enriched definitions return to Databricks, ready to apply in your environment. Genie and your agents operate on mastered semantics. Native connector sync is on the roadmap.
Master your semantic models, in Databricks and beyond
See how Alation unifies Databricks metric views with semantic models from Snowflake, Power BI, Looker, Cube, and more.
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