How to connect and process SQL Server Data from Azure Databricks

Databricks is a cloud-based service that provides data processing capabilities through Apache Spark. When paired with the CData JDBC Driver, customers can use Databricks to perform data engineering and data science on live SQL Server data. This article walks through hosting the CData JDBC Driver in Azure, as well as connecting to and processing live SQL Server data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live SQL Server data. When you issue complex SQL queries to SQL Server, the driver pushes supported SQL operations, like filters and aggregations, directly to SQL Server and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations). Its built-in dynamic metadata querying allows you to work with and analyze SQL Server data using native data types.

Install the CData JDBC Driver in Azure

To work with live SQL Server data in Databricks, install the driver on your Azure cluster.

1. Navigate to your Databricks administration screen and select the target cluster.
2. On the Libraries tab, click "Install New."
3. Select "Upload" as the Library Source and "Jar" as the Library Type.
4. Upload the JDBC JAR file (cdata.jdbc.sql.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

Connect to SQL Server from Databricks

With the JAR file installed, we are ready to work with live SQL Server data in Databricks. Start by creating a new notebook in your workspace. Name the notebook, select Python as the language (though Scala is available as well), and choose the cluster where you installed the JDBC driver. When the notebook launches, we can configure the connection, query SQL Server, and create a basic report.

Configure the Connection to SQL Server

Connect to SQL Server by referencing the class for the JDBC Driver and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the RTK property in the JDBC URL (unless you are using a Beta driver). You can view the licensing file included in the installation for information on how to set this property.

driver = "cdata.jdbc.sql.SQLDriver"
url = "jdbc:sql:RTK=5246...;User=myUser;Password=myPassword;Database=NorthWind;Server=myServer;Port=1433;"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the SQL Server JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

Fill in the connection properties and copy the connection string to the clipboard.

Connecting to Microsoft SQL Server

Connect to Microsoft SQL Server using the following properties:

• Server: The name of the server running SQL Server.
• User: The username provided for authentication with SQL Server.
• Password: The password associated with the authenticating user.
• Database: The name of the SQL Server database.

Connecting to Azure SQL Server and Azure Data Warehouse

You can authenticate to Azure SQL Server or Azure Data Warehouse by setting the following connection properties:

• Server: The server running Azure. You can find this by logging into the Azure portal and navigating to "SQL databases" (or "SQL data warehouses") -> "Select your database" -> "Overview" -> "Server name."
• User: The name of the user authenticating to Azure.
• Password: The password associated with the authenticating user.
• Database: The name of the database, as seen in the Azure portal on the SQL databases (or SQL warehouses) page.

Load SQL Server Data

Once the connection is configured, you can load SQL Server data as a dataframe using the CData JDBC Driver and the connection information.

remote_table = spark.read.format ( "jdbc" ) \
	.option ( "driver" , driver) \
	.option ( "url" , url) \
	.option ( "dbtable" , "Orders") \
	.load ()

Display SQL Server Data

Check the loaded SQL Server data by calling the display function.

display (remote_table.select ("ShipName"))

Analyze SQL Server Data in Azure Databricks

If you want to process data with Databricks SparkSQL, register the loaded data as a Temp View.

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

The SparkSQL below retrieves the SQL Server data for analysis.

% sql

SELECT ShipName, Freight FROM Orders

The data from SQL Server is only available in the target notebook. If you want to use it with other users, save it as a table.

remote_table.write.format ( "parquet" ) .saveAsTable ( "SAMPLE_TABLE" )

Download a free, 30-day trial of the CData JDBC Driver for SQL Server and start working with your live SQL Server data in Azure Databricks. Reach out to our Support Team if you have any questions.