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# Query Databricks metric views in Sigma (Beta)

This documentation describes one or more public beta features that are in development. Beta features are subject to quick, iterative changes; therefore the current user experience in the Sigma service can differ from the information provided in this page.

This page should not be considered official published documentation until Sigma removes this notice and the beta flag on the corresponding feature(s) in the Sigma service. For the full beta feature disclaimer, see [Beta features](/docs/sigma-product-releases#beta-features).

Databricks Unity Catalog metric views let you define governed, reusable business logic in Databricks. A metric view allows you to model metrics, dimensions, and joins in a declarative format that BI tools and AI agents can query with standard SQL.

When you connect a Databricks workspace to Sigma, metric views in that workspace become available in Sigma. You can use the metrics, dimensions, and related columns defined in a metric view to do the following in Sigma:

* Build a table in a data model using a metric view as a source.
* Add tables, pivot tables, and charts to a workbook that use a metric view as a source.
* Join, union, or transpose metric view elements with other elements.
* Browse metric views in the data catalog.
* Query a metric view with custom SQL.

For more details about Databricks metric views, see <a href="https://docs.databricks.com/aws/en/metric-views/" target="_blank">Unity Catalog metric views</a> in the Databricks documentation.

## Limitations

The following is not yet supported:

* Metric views that contain window measures, including range and semi-additive aggregations.

When working with metric views in Sigma, the following limitations apply:

* Custom SQL queries cannot reference a Sigma element sourced from a metric view using the `sigma_element()` function. Query the metric view directly instead.

## Requirements

The following privileges must be granted in Databricks to each user querying metric views in Sigma (if the Databricks connection uses OAuth) or to the user or service principal associated with the connection (if the connection uses a personal access token):

* `SELECT` on each metric view you want to work with in Sigma.
* `SELECT` on the underlying tables referenced by the metric views you want to work with in Sigma.

For more details on granting privileges, see <a href="https://docs.databricks.com/aws/en/business-semantics/metric-views/manage#set-permissions" target="_blank">Set permissions</a> in the Databricks documentation.

## Browse a Databricks metric view in the data catalog

If a metric view is defined in the Databricks workspace connected to Sigma, you can use the [data catalog](/docs/manage-data-catalog) to browse the view as a table, as well as the dimensions, metrics, and joins it defines:

To access the data catalog in Sigma, you must have the **View connections** permission enabled for your account type.

1. From Sigma Home, select **Connections** to open the list of connected data sources.

2. Select the data connection you want to browse.

3. In the left navigation panel, search or browse to locate the metric view.

4. Select the metric view to view more details:

   * On the **Overview** tab, review the metric view's columns and data as a table.
   * On the **Columns** tab, review column names, data types, formats, and descriptions.
   * On the **Metrics** tab, review the names, formulas, formats, and descriptions of any metrics defined on the view.
   * On the **Joins** tab, review the lineage of tables joined to the base table in the metric view's definition.
   * On the **Access** tab, review the users and teams with access to the metric view as a data source.

5. Click **Explore** to open the metric view in an exploration.

## Use a Databricks metric view as a data source

To use a metric view as a source for a data element:

1. Open a data model or workbook for editing.
2. Add a data element to your document.
3. Browse to and select the metric view in your Databricks connection to use as the element source.

   When you select a metric view, all dimensions from the view are added to the element as source columns, and metrics from the view become accessible in the element's metrics panel.

After you add a metric view table as a source, you can:

* Add columns to the element from related tables.
* Use metrics defined on the metric view in calculations and charts.
* Join, union, or transpose the element with other elements.
* Use the element as a source for child elements.

## Query a metric view with custom SQL

To query a metric view with custom SQL, list the dimensions you want in the output in the `SELECT` statement and wrap your desired measures with the `MEASURE()` aggregate function.

For example, given a Databricks metric view with a definition that matches the example in the Databricks documentation, <a href="https://docs.databricks.com/aws/en/business-semantics/metric-views/tpch-example" target="_blank">Metric views: TPCH example</a>, with the following characteristics:

* A metric view called `tpch_sales_analytics`.
* A source table of `samples.tpch.orders` with joins to customer and nation tables.
* Dimensions, for example, `order_date`, `customer_nation`, and `market_segment`.
* Measures, for example, `order_count` and `open_order_revenue`.

The following query returns the order count and open order revenue for each `order_date`:

```sql
SELECT
  order_date,
  MEASURE(order_count),
  MEASURE(open_order_revenue)
FROM catalog.schema.tpch_sales_analytics
GROUP BY order_date
```

You cannot use a `SELECT *` statement to query a metric view.

For more details, see <a href="https://docs.databricks.com/aws/en/business-semantics/metric-views/query" target="_blank">Query metric views</a> in the Databricks documentation.

For more information about using custom SQL in Sigma, see [Write custom SQL](/docs/write-custom-sql).