How to work with Salesforce Pardot 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 Pardot, Spark can work with live Salesforce Pardot data. This article describes how to connect to and query Salesforce Pardot data from a Spark shell.

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

Install the CData JDBC Driver for Salesforce Pardot

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

Start a Spark Shell and Connect to Salesforce Pardot Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Salesforce Pardot/lib/cdata.jdbc.salesforcepardot.jar
   

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

   Salesforce Pardot supports connecting through API Version, Username, Password and User Key.

   • ApiVersion: The Salesforce Pardot API version which the provided account can access. Defaults to 4.
   • User: The Username of the Salesforce Pardot account.
   • Password: The Password of the Salesforce Pardot account.
   • UserKey: The unique User Key for the Salesforce Pardot account. This key does not expire.
   • IsDemoAccount (optional): Set to TRUE to connect to a demo account.

   Accessing the Pardot User Key

   The User Key of the current account may be accessed by going to Settings -> My Profile, under the API User Key row.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.salesforcepardot.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 Pardot, using the connection string generated above.

   scala> val salesforcepardot_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:salesforcepardot:ApiVersion=4;User=YourUsername;Password=YourPassword;UserKey=YourUserKey;").option("dbtable","Prospects").option("driver","cdata.jdbc.salesforcepardot.SalesforcePardotDriver").load()
   

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

4. Register the Salesforce Pardot data as a temporary table:

   scala> salesforcepardot_df.registerTable("prospects")

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

   scala> salesforcepardot_df.sqlContext.sql("SELECT Id, Email FROM Prospects WHERE ProspectAccountId = 703").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 Pardot in Apache Spark, you are able to perform fast and complex analytics on Salesforce Pardot 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.