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

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

Install the CData JDBC Driver for Oracle Eloqua

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

Start a Spark Shell and Connect to Oracle Eloqua Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Oracle Eloqua/lib/cdata.jdbc.oracleeloqua.jar
   

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

   There are two authentication methods available for connecting to Oracle Eloqua: Login and OAuth. The Login method requires you to have the Company, User, and Password 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. OAuth is better suited for allowing other users to access their own data. Using login credentials is better suited for accessing your own data.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.oracleeloqua.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 Oracle Eloqua, using the connection string generated above.

   scala> val oracleeloqua_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:oracleeloqua:User=user;Password=password;Company=CData;").option("dbtable","Campaign").option("driver","cdata.jdbc.oracleeloqua.OracleEloquaDriver").load()
   

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

4. Register the Oracle Eloqua data as a temporary table:

   scala> oracleeloqua_df.registerTable("campaign")

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

   scala> oracleeloqua_df.sqlContext.sql("SELECT Name, ActualCost FROM Campaign WHERE ShipCity = New York").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 Oracle Eloqua in Apache Spark, you are able to perform fast and complex analytics on Oracle Eloqua 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.