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

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

Install the CData JDBC Driver for Campaigner

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

Start a Spark Shell and Connect to Campaigner Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Campaigner/lib/cdata.jdbc.api.jar
   

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

   Start by setting the Profile connection property to the location of the Campaigner Profile on disk (e.g. C:\profiles\Campaigner.apip). Next, set the ProfileSettings connection property to the connection string for Campaigner (see below).

   Campaigner API Profile Settings

   Sign into your Campaigner account and navigate to Account Settings > Users, create a new API User role, and save to generate the API key.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.api.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 Campaigner, using the connection string generated above.

   scala> val api_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:api:Profile=C:\profiles\Campaigner.apip;ProfileSettings='APIKey=your_api_key';").option("dbtable","ActiveCampaigns").option("driver","cdata.jdbc.api.APIDriver").load()
   

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

4. Register the Campaigner data as a temporary table:

   scala> api_df.registerTable("activecampaigns")

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

   scala> api_df.sqlContext.sql("SELECT Id, Name FROM ActiveCampaigns WHERE Status = active").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 Campaigner in Apache Spark, you are able to perform fast and complex analytics on Campaigner 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.