# Browse data using SQL extension
<a name="sagemaker-sql-extension-features-data-discovery"></a>

To open the SQL extension user interface (UI), choose the SQL extension icon (![\[Purple circular icon with a clock symbol representing time or scheduling.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/studio/sqlexplorer/sqlexplorer-icon.png)) in the navigation pane of your JupyterLab application in Studio. The left panel data discovery view expands and displays all pre-configured data store connections to Amazon Athena, Amazon Redshift, and Snowflake.

From there, you can:
+ Expand a specific connection to explore its databases, schemas, tables or views, and columns.
+ Search for a specific connection using the search box in the SQL extension UI. The search returns any databases, schemas, tables, or views that partially match the string you enter.

**Note**  
If Athena is already set up in your AWS account, you can enable a `default-athena-connection` in your JupyterLab application. This allows you to run Athena queries without needing to manually create the connection. To enable the default Athena connection:  
Check with your administrator that your execution role has the required permissions to access Athena and the AWS Glue catalog. For details on the permissions required, see [Configure an AWS Glue connection for Athena](sagemaker-sql-extension-datasources-glue-connection.md#sagemaker-sql-extension-athena-glue-connection-config)
In your JupyterLab application, navigate to the **Settings** menu in the top navigation bar and open the **Settings Editor** menu.
Choose **Data Discovery**.
Check the box for **Enable default Athena connection**.
You can update the default `primary` WorkGroup if needed.

To query a database, schema, or table in a JupyterLab notebook, from a given connection in the SQL extension pane:
+ Choose the three dots icon (![\[SQL extension three dots icon.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/studio/sqlexplorer/sqlexplorer-3dots-icon.png)) on the right side of any database, schema, or table.
+ Select **Query in notebook** from the menu.

  This automatically populates a notebook cell in JupyterLab with the relevant `%%sm_sql` magic command to connect to the data source. It also adds a sample SQL statement to help you start querying right away. You can further refine the SQL query using the auto-complete and highlighting features of the extension. See [SQL editor features of the JupyterLab SQL extension](sagemaker-sql-extension-features-editor.md) for more information on using the SQL extension SQL editor.

At the table level, the three dots icon provides the additional option to choose to **Preview** a table's metadata.

The JupyterLab notebook cell content below shows an example of what is automatically generated when selecting the **Query in notebook** menu on a `redshift-connection` data source in the SQL extension pane.

```
%%sm_sql --metastore-id redshift-connection --metastore-type GLUE_CONNECTION

-- Query to list tables from schema 'dev.public'
SHOW TABLES
FROM
  SCHEMA "dev"."public"
```

Use the *less than* symbol (![\[Icon to clear the SQL extension search box.\]](http://docs.aws.amazon.com/sagemaker/latest/dg/images/studio/sqlexplorer/sqlexplorer-search-clear.png)) at the top of the SQL extension pane to clear the search box or return to the list of your connections.

**Note**  
The extension caches your exploration results for fast access. If the cached results are outdated or a connection is missing from your list, you can manually refresh the cache by choosing the **Refresh** button at the bottom of the SQL extension panel. For more information on connection caching, see [SQL extension connection caching](sagemaker-sql-extension-features-connection-caching.md).