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

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

Install the CData JDBC Driver for Zendesk

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

Start a Spark Shell and Connect to Zendesk Data

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

   $ spark-shell --jars /CData/CData JDBC Driver for Zendesk/lib/cdata.jdbc.zendesk.jar
   

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

   Connecting to Zendesk

   To connect, set the URL and provide authentication. The URL is your Zendesk Support URL: https://{subdomain}.zendesk.com.

   Authenticating to Zendesk

   You can authenticate using the Basic or OAuth methods.

   Using Basic Authentication

   To use Basic authentication, specify your email address and password or your email address and an API token. Set User to your email address and follow the steps below to provide the Password or ApiToken.

   • Enable password access in the Zendesk Support admin interface at Admin > Channels > API.
   • Manage API tokens in the Zendesk Support Admin interface at Admin > Channels > API. More than one token can be active at the same time. Deleting a token deactivates it permanently.

   Using OAuth Authentication

   See the Getting Started guide in the CData driver documentation for an authentication guide.

   Built-in Connection String Designer

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

   java -jar cdata.jdbc.zendesk.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 Zendesk, using the connection string generated above.

   scala> val zendesk_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:zendesk:URL=https://subdomain.zendesk.com;[email protected];Password=test123;InitiateOAuth=GETANDREFRESH;").option("dbtable","Tickets").option("driver","cdata.jdbc.zendesk.ZendeskDriver").load()
   

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

4. Register the Zendesk data as a temporary table:

   scala> zendesk_df.registerTable("tickets")

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

   scala> zendesk_df.sqlContext.sql("SELECT Id, Subject FROM Tickets WHERE Industry = Floppy Disks").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 Zendesk in Apache Spark, you are able to perform fast and complex analytics on Zendesk 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.