How to connect and process Salesforce Data Cloud Data from Azure Databricks



Use CData, Azure, and Databricks to perform data engineering and data science on live Salesforce Data Cloud Data

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 Salesforce Data Cloud data. This article walks through hosting the CData JDBC Driver in Azure, as well as connecting to and processing live Salesforce Data Cloud data in Databricks.

With built-in optimized data processing, the CData JDBC driver offers unmatched performance for interacting with live Salesforce Data Cloud data. When you issue complex SQL queries to Salesforce Data Cloud, the driver pushes supported SQL operations, like filters and aggregations, directly to Salesforce Data Cloud 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 Salesforce Data Cloud data using native data types.

Install the CData JDBC Driver in Azure

To work with live Salesforce Data Cloud 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 "DBFS" as the Library Source and "JAR" as the Library Type.
  4. Upload the JDBC JAR file (cdata.jdbc.salesforcedatacloud.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for Salesforce Data Cloud\lib).

Connect to Salesforce Data Cloud from Databricks

With the JAR file installed, we are ready to work with live Salesforce Data Cloud data in Databricks. Start by creating a new notebook in your workspace. Name the workbook, make sure Python is selected as the language (which should be by default), click on Connect and under General Compute select the cluster where you installed the JDBC driver (should be selected by default).

Configure the Connection to Salesforce Data Cloud

Connect to Salesforce Data Cloud 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.salesforcedatacloud.SalesforceDataCloudDriver"
url = "jdbc:salesforcedatacloud:RTK=5246...;InitiateOAuth=GETANDREFRESH"

Built-in Connection String Designer

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

java -jar cdata.jdbc.salesforcedatacloud.jar

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

Salesforce Data Cloud supports authentication via the OAuth standard.

OAuth

Set AuthScheme to OAuth.

Desktop Applications

CData provides an embedded OAuth application that simplifies authentication at the desktop.

You can also authenticate from the desktop via a custom OAuth application, which you configure and register at the Salesforce Data Cloud console. For further information, see Creating a Custom OAuth App in the Help documentation.

Before you connect, set these properties:

  • InitiateOAuth: GETANDREFRESH. You can use InitiateOAuth to avoid repeating the OAuth exchange and manually setting the OAuthAccessToken.
  • OAuthClientId (custom applications only): The Client ID assigned when you registered your custom OAuth application.
  • OAuthClientSecret (custom applications only): The Client Secret assigned when you registered your custom OAuth application.

When you connect, the driver opens Salesforce Data Cloud's OAuth endpoint in your default browser. Log in and grant permissions to the application.

The driver then completes the OAuth process as follows:

  • Extracts the access token from the callback URL.
  • Obtains a new access token when the old one expires.
  • Saves OAuth values in OAuthSettingsLocation so that they persist across connections.
  • For other OAuth methods, including Web Applications and Headless Machines, refer to the Help documentation.

    Load Salesforce Data Cloud Data

    Once the connection is configured, you can load Salesforce Data Cloud 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" , "Account") \
    	.load ()
    

    Display Salesforce Data Cloud Data

    Check the loaded Salesforce Data Cloud data by calling the display function.

    display (remote_table.select ("[Account ID]"))
    

    Analyze Salesforce Data Cloud 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 Salesforce Data Cloud data for analysis.

    result = spark.sql("SELECT [SAMPLE_VIEW ID], [SAMPLE_VIEW Name] FROM SAMPLE_VIEW WHERE EmployeeCount > 250")
    

    The data from Salesforce Data Cloud 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 Salesforce Data Cloud and start working with your live Salesforce Data Cloud data in Azure Databricks. Reach out to our Support Team if you have any questions.

Ready to get started?

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