Working with Databricks SQL Warehouse to Enable Relational Multidimensional Analytics Queries on Azure

The Kyvos 2023.5 release support for Databricks SQL warehouse to enable the execution of relational multidimensional analytics queries.

Note

  • Supported only with premium workspace.

  • Supported only with Personal Access Token authentication.

  • On storage, Storage Blob Data Contributor rights are required for the logged-in user. 

  • You must have permission to create (and map) Storage credentials and External Locations for the Unity Catalog.

Configuring Databricks SQL warehouse

  • To create Databricks SQL warehouse, refer to Microsoft Documentation.

    1. Type: The serverless type will only be supported.

    2. Unity Catalog must be enabled. If Unity Catalog is not enabled for your workspace, you do not see this option.

  • To configure Databricks SQL warehouses with SQL parameters, perform the following steps.

    1. Open Databricks workspace.

    2. Click your username in the top bar of the workspace and select Admin Settings from the list.

    3. Click the SQL Warehouse Settings tab.

    4. In the SQL Configuration Parameters textbox, specify the below key-value pair:
      LEGACY_TIME_PARSER_POLICY LEGACY

    5. Click Save.
      For more information, see the SQL configuration parameters and Legacy_Time_Parser_Policy

  • You can modifying the existing default Datalake connection by selecting the SQL engine as Database SQL Warehouse .
    See the Provider parameters table for details.

    1. Enter Databricks SQL Warehouse JDBC URL. For more information, see Microsoft documentation.

    2. Enter Databricks SQL Personal Access Token for the Databricks SQL workspace.
      NOTE: It is optional in the absence of this PAT. The system will attempt to connect using the PAT provided for the Databricks connection.

    3. Select the Catalog Enabled checkbox. It is mandatory to enabled it for Databricks SQL.

  • You can also create a new build and read connection by using the SQL engine as Database SQL Warehouse. To do this, refer to the section, as explained below.

Creating Databricks SQL Warehouse connection

  1. From the Toolbox, click Setup, then choose Connections.

  2. From the Actions menu ( ⋮ ) click Add Connection.

  3. Enter a Name for the connection.

  4. From the Category drop-down list, select the BUILD option.

  5. From the Provider list, select the Databricks option, and provide the following:

    1. Databricks Cluster Id: Enter the ID of your Databricks cluster.
      To obtain this ID, click the Cluster Name on the Clusters page in Databricks. The page URL shows <https://<databricks-instance>/#/settings/clusters/<cluster-id>

    2. Databricks Service Address: Enter the URL of your Databricks workspace.

    3. Databricks Personal Access Token: Enter the personal access token to access and connect to your Databricks workspace. Refer to the Databricks documentation to get your token 

  6. To use this connection as a read connection also, select the Use as Source checkbox.

  7. Select the SQL engine as Database SQL Warehouse .
    See the Provider parameters table for details.

    1. Enter Databricks SQL Warehouse JDBC URL. For more information, see Microsoft documentation.

    2. Enter Databricks SQL Personal Access Token for the Databricks SQL workspace.
      NOTE: It is optional in the absence of this PAT. The system will attempt to connect using the PAT provided for the Databricks connection.

  8. To use this connection as the default SQL engine, select the Is Default SQL Engine checkbox.

  9. Select the Catalog Enabled checkbox. It is mandatory to enabled it for Databricks SQL.

  10. Click the Properties link to view or set properties.

  11. After configuring the settings, click the Save button. 

  12. To refresh connections, click the Actions menu ( ⋮ ) at the top of the Connections column and select Refresh.

 

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