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Process & Analyze Salesforce Data in Azure Databricks



Host the CData JDBC Driver for Salesforce in Azure and use Databricks to perform data engineering and data science on live Salesforce 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. This article walks through hosting the CData JDBC Driver in Azure, as well as connecting to and processing live Salesforce data in Databricks.

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

Install the CData JDBC Driver in Azure

To work with live Salesforce 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.salesforce.jar) from the installation location (typically C:\Program Files\CData\CData JDBC Driver for Salesforce\lib).

Connect to Salesforce from Databricks

With the JAR file installed, we are ready to work with live Salesforce 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 Salesforce, and create a basic report.

Configure the Connection to Salesforce

Connect to Salesforce 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.salesforce.SalesforceDriver"
url = "jdbc:salesforce:RTK=5246...;User=username;Password=password;SecurityToken=Your_Security_Token;"

Built-in Connection String Designer

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

java -jar cdata.jdbc.salesforce.jar

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

There are several authentication methods available for connecting to Salesforce: Login, OAuth, and SSO. The Login method requires you to have the username, password, and security token of the user.

If you do not have access to the username and password or do not wish to require them, you can use OAuth authentication.

SSO (single sign-on) can be used by setting the SSOProperties, SSOLoginUrl, and TokenUrl connection properties, which allow you to authenticate to an identity provider. See the "Getting Started" chapter in the help documentation for more information.

Load Salesforce Data

Once the connection is configured, you can load Salesforce 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

Check the loaded Salesforce data by calling the display function.

display (remote_table.select ("Industry"))

Analyze Salesforce 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 for analysis.

% sql

SELECT Contact.Name, SUM(Account.AnnualRevenue) FROM Contact, Account GROUP BY Contact.Name

The data from Salesforce 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 and start working with your live Salesforce data in Apache NiFi. Reach out to our Support Team if you have any questions.