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If your team defines metrics in Databricks Unity Catalog, you can sync those definitions into Omni so that your metric views are available for querying, visualization, and AI-powered exploration — without duplicating any business logic.

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How it works

When you enable the integration and run a schema refresh, Omni pulls the structure of your Databricks metric views into the semantic model. Here’s what that looks like in practice:
  • Metric views become topics. Each metric view’s dimensions and measures are imported as fields in Omni. Users can query them with the visual query builder or SQL and add results to dashboards without recreating any logic.
  • Comments become descriptions. Any comments defined on fields in your Databricks metric views are mapped to field descriptions in the Omni semantic layer.
  • Lineage is preserved. Fields managed by the Databricks semantic layer are tagged with a comment indicating that the definition is managed by the Databricks semantic view, so users can see where a field’s logic comes from. In Omni, it will look similar to the following:
    Example Databricks managed comment
    average_sale_price:
  # Measure is defined in the semantic view order_items_uc_metrics in the data warehouse
  sql: AVG(`sale_price`)
  description: this is the average of order sale price
  • Changes sync automatically. When your team updates a metric view in Databricks — for example, adding an average_sale_price measure — the change appears in Omni after the next schema refresh.
  • AI features understand the context. Because the synced metadata includes field definitions and descriptions, Omni’s AI features can answer natural language questions about your Databricks metrics without users needing to write SQL.

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Limitations

  • Measure-level filters are not currently supported as filtered measures
  • Window measures in Databricks are not generated during sync
  • Snowflake schema joins (nested dimensions) are not supported

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Requirements

To follow the steps in this guide, you’ll need:
  • An existing Databricks connection configured in Omni
  • Connection Admin or Organization Admin permissions in Omni
  • Existing metric views defined in your Databricks Unity Catalog

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Setup

1

Enable the integration

  1. In Omni, navigate to Settings > Connections.
  2. Click the Databricks connection you want to work with.
  3. In the Settings tab, check the Enable DW metric view integration box.
  4. Click Update and Test connection.
2

Refresh the model's schema

After you enable the integration, you’ll need to trigger a schema refresh to pull the Databricks definitions into your Omni model.
  1. Click Develop in the left sidebar.
  2. Click the Databricks-based model you want to work with. This opens the model IDE.
  3. Click Model > Refresh schema.
After the refresh completes, your Databricks metric views will appear as topics in the model. You can open the topic in the model IDE to view its structure or add additional Omni topic parameters.

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Pushing Omni topics into Databricks

You can also go in the other direction — if you’ve built a topic in Omni that you want to formalize as a governed metric view in your Unity Catalog, you can use the Omni Python SDK to generate the required Databricks DDL. You can then run the generated SQL in the Databricks console to create the metric view. For a full walkthrough, refer to the Push Omni topics to Databricks as metric views guide.