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

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

Install the CData JDBC Driver for Vault CRM

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

Start a Spark Shell and Connect to Vault CRM Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Vault CRM/lib/cdata.jdbc.vaultcrm.jar
   

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

   You are ready to connect after specifying the following connection properties:

   • Url: The host you see in the URL after you login to your account. For example: https://my-veeva-domain.veevavault.com
   • User: The username you use to login to your account.
   • Password: The password you use to login to your account.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.vaultcrm.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 Vault CRM, using the connection string generated above.

   scala> val vaultcrm_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:vaultcrm:User=myuser;Password=mypassword;Server=localhost;Database=mydatabase;").option("dbtable","NorthwindProducts").option("driver","cdata.jdbc.vaultcrm.VaultCRMDriver").load()
   

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

4. Register the Vault CRM data as a temporary table:

   scala> vaultcrm_df.registerTable("northwindproducts")

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

   scala> vaultcrm_df.sqlContext.sql("SELECT ProductId, ProductName FROM NorthwindProducts WHERE CategoryId = 5").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 Vault CRM in Apache Spark, you are able to perform fast and complex analytics on Vault CRM 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.