How to work with Salesforce Data Cloud Data in Apache Spark using SQL

Apache Spark is a fast and general engine for large-scale data processing. When paired with the CData JDBC Driver for Salesforce Data Cloud, Spark can work with live Salesforce Data Cloud data. This article describes how to connect to and query Salesforce Data Cloud data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live Salesforce Data Cloud data due to optimized data processing built into the driver. 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 (often SQL functions and JOIN operations) client-side. With built-in dynamic metadata querying, you can work with and analyze Salesforce Data Cloud data using native data types.

Install the CData JDBC Driver for Salesforce Data Cloud

Download the CData JDBC Driver for Salesforce Data Cloud installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to Salesforce Data Cloud Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for Salesforce Data Cloud JAR file as the jars parameter:

   $ spark-shell --jars /CData/CData JDBC Driver for Salesforce Data Cloud/lib/cdata.jdbc.salesforcedatacloud.jar
   

2. With the shell running, you can connect to Salesforce Data Cloud with a JDBC URL and use the SQL Context load() function to read a table.

   Salesforce Data Cloud supports authentication via the OAuth standard.

   OAuth

   Set 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:

   • : GETANDREFRESH. You can use to avoid repeating the OAuth exchange and manually setting the .
   • (custom applications only): The Client ID assigned when you registered your custom OAuth application.
   • (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 so that they persist across connections.

   For other OAuth methods, including Web Applications and Headless Machines, refer to the Help documentation.

   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.

   👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)
   

   Configure the connection to Salesforce Data Cloud, using the connection string generated above.

   scala> val salesforcedatacloud_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:salesforcedatacloud:InitiateOAuth=GETANDREFRESH;").option("dbtable","Account").option("driver","cdata.jdbc.salesforcedatacloud.SalesforceDataCloudDriver").load()
   

3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the Salesforce Data Cloud data as a temporary table:

   scala> salesforcedatacloud_df.registerTable("account")

5. Perform custom SQL queries against the Data using commands like the one below:

   scala> salesforcedatacloud_df.sqlContext.sql("SELECT [Account ID], [Account Name] FROM Account WHERE EmployeeCount = 250").collect.foreach(println)

   You will see the results displayed in the console, similar to the following:

   👁 Data in Apache Spark (Salesforce is shown)
   

Using the CData JDBC Driver for Salesforce Data Cloud in Apache Spark, you are able to perform fast and complex analytics on Salesforce Data Cloud data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the hundreds of CData JDBC Drivers and get started today.