Working with Databricks SQL Warehouse on AWS

Prerequisites for creating AWS SQL Warehouse Connection

Before creating an AWS SQL Warehouse connection without Spark, complete the following prerequisites.

Unity Catalog must be enabled on your Databricks cluster.
To create a storage credential for connecting to AWS S3, refer to Databricks documentation.
Fetch the S3 path till the parent directory of your respective storage account.
On the Unity Catalog page, go to external locations and create a new external location by providing an external location name, newly created Storage credential, and fetched s3 URL.
The URL format must be in s3://bucket name/<path> For example s3://kyvos-output-56555/user/engine_work
Test the newly created external location by clicking the 'Test connection' button. This will validate the connection with the external location path. Through the permissions tab, assign your user the CREATE EXTERNAL TABLE and WRITE FILES role.
Go to semantic model Advanced Properties, and add the following properties:
1. kyvos.sqlwarehouse.catalog: Enter the temp catalog name to create a parquet table.
2. kyvos.sqlwarehouse.tempdb: Enter the temp database name to create a parquet table.
You must have 'create table and Use schema' permissions on the temp catalog.

You can create AWS Databricks SQL warehouse connection with no-Spark.

Note

The steps for Working with Databricks SQL Warehouse with Personal Token are the same.

Supported for AWS and Azure.
Supported only with premium workspace.
The OAuth connectivity with Databricks SQL Warehouse for AWS no-Spark is not supported.
The serverless type Databricks SQL Warehouse cluster is only supported.
Unity Catalog must be enabled.

To create AWS SQL Warehouse connection for processing semantic models with No-Spark, perform the following steps.

Parameter/Field	Comments/Description
Name	Enter a unique name for the connection.
Category	Select the Warehouse option. There may be more than one warehouse connection.
Provider	Select DATABRICKSSQL from the list.
Driver class	This field will be auto selected. The Driver class (com.databricks.client.jdbc.Driver ) is required to connect to the Databricks SQL.
URL	In the URL field, enter the Databricks SQL Warehouse JDBC URL. For more information, see Microsoft documentation.
Authentication Type	Select Personal Access Token.
User Name	Enter the text token for username.
Password	Enter Databricks SQL Personal Access Token’s value for the Databricks SQL workspace
Use as Source	This checkbox is auto selected. Enter Spark Read Method as JDBC
Is default SQL Engine	By default, the checkbox is selected as this connection can only be used to read data (creating datasets) on which the semantic model will be created.
Catalog Enabled	Select this checkbox to list different catalog created in the workspace.
Properties	Click Properties to view or set properties.

After you finish configuring the settings using the table shown below the screenshot, click the Test button from the top left to validate the connection settings.
If the connection is valid, click the Save button.

To create AWS SQL Warehouse connection for sanity suite, perform the following steps.

Parameter	Description

Parameter	Description
Name	Enter SanityConnection as a name.
Category	Select the Warehouse option.
Provider	Select the Generic option.
Driver	Enter the Driver class as com.databricks.client.jdbc.Driver
URL	Enter JDBC Databricks URL.
Username	Enter Databricks token.
Password	Enter Databricks token.
Authentication Type	Select Personal Access Token.
Use as Source	This checkbox is auto selected. Enter Spark Read Method as JDBC.
Is Default SQL Engine	To enable the connection for raw data, click the Is Default SQL Engine checkbox to set this connection to run the default SQL engine.
Properties	Click Properties to view or set properties.
Catalog Enabled	Select this checkbox to list different catalog created in the workspace.