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

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

Install the CData JDBC Driver for Oracle Sales

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

Start a Spark Shell and Connect to Oracle Sales Data

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

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

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

   Oracle Sales uses Basic authentication over SSL; after setting the following connection properties, you are ready to connect:

   • Username: Set this to the user name that you use to log into your Oracle Cloud service.
   • Password: Set this to your password.
   • HostURL: Set this to the Web address (URL) of your Oracle Cloud service.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.oraclesalescloud.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 Sales, using the connection string generated above.

   scala> val oraclesalescloud_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:oraclesalescloud:HostURL=https://my.host.oraclecloud.com; Username=abc123; Password=abcdef;").option("dbtable","Opportunities").option("driver","cdata.jdbc.oraclesalescloud.OracleSalesCloudDriver").load()
   

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

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

   scala> oraclesalescloud_df.registerTable("opportunities")

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

   scala> oraclesalescloud_df.sqlContext.sql("SELECT OptyId, Name FROM Opportunities WHERE CreatedBy = Jack").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 Sales in Apache Spark, you are able to perform fast and complex analytics on Oracle Sales 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.