Work with Data in Apache Spark Using SQL

Ready to get started?

Download a free trial:

Download Now

Learn more: JDBC Driver

Rapidly create and deploy powerful Java applications that integrate with

Access and process Data in Apache Spark using the CData JDBC Driver.

Apache Spark is a fast and general engine for large-scale data processing. When paired with the CData JDBC Driver for, Spark can work with live data. This article describes how to connect to and query data from a Spark shell.

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

Install the CData JDBC Driver for

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

Start a Spark Shell and Connect to Data

  1. Open a terminal and start the Spark shell with the CData JDBC Driver for JAR file as the jars parameter: $ spark-shell --jars /CData/CData JDBC Driver for
  2. With the shell running, you can connect to with a JDBC URL and use the SQL Context load() function to read a table.

    You must use OAuth to authenticate with Outreach. Set the InitiateOAuth connection property to "GETANDREFRESH". For more information, refer to the OAuth section in the Help documentation.

    Built-in Connection String Designer

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

    java -jar cdata.jdbc.outreach.jar

    Fill in the connection properties and copy the connection string to the clipboard.

    Configure the connection to, using the connection string generated above.

    scala> val outreach_df ="jdbc").option("url", "jdbc:outreach:").option("dbtable","Accounts").option("driver","cdata.jdbc.outreach.OutreachDriver").load()
  3. Once you connect and the data is loaded you will see the table schema displayed.
  4. Register the data as a temporary table:

    scala> outreach_df.registerTable("accounts")
  5. Perform custom SQL queries against the Data using commands like the one below:

    scala> outreach_df.sqlContext.sql("SELECT Name, NumberOfEmployees FROM Accounts WHERE Industry = Textiles").collect.foreach(println)

    You will see the results displayed in the console, similar to the following:

Using the CData JDBC Driver for in Apache Spark, you are able to perform fast and complex analytics on data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the 200+ CData JDBC Drivers and get started today.