Process & Analyze Zendesk Data in Databricks (AWS)

Databricks is a cloud-based service that provides data processing capabilities through Apache Spark. When paired with the CData JDBC Driver, customers can use Databricks to perform data engineering and data science on live Zendesk data. This article explains how to host the CData JDBC Driver in AWS, as well as connect to and process live Zendesk data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live Zendesk data. 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 client-side (often SQL functions and JOIN operations). Its built-in dynamic metadata querying allows you to work with and analyze Zendesk data using native data types.

Install the CData JDBC Driver in Databricks

To work with live Zendesk data in Databricks, install the driver on your Databricks cluster.

1. Navigate to your Databricks administration screen and select the target cluster.
2. On the Libraries tab, click "Install New."
3. Select "Upload" as the Library Source and "Jar" as the Library Type.
4. Upload the JDBC JAR file (cdata.jdbc.zendesk.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

👁 Loading the JDBC JAR File into AWS

Access Zendesk Data in your Notebook: Python

With the JAR file installed, we are ready to work with live Zendesk data in Databricks. Start by creating a new notebook in your workspace. Name the notebook, select Python as the language (though Scala is available as well), and choose the cluster where you installed the JDBC driver. When the notebook launches, we can configure the connection, query Zendesk, and create a basic report.

Configure the Connection to Zendesk

Connect to Zendesk by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the property in the JDBC URL (unless you are using a Beta driver). You can view the licensing file included in the installation for information on how to set this property.

Step 1: Connection Information

driver = "cdata.jdbc.zendesk.ZendeskDriver"
url = "jdbc:zendesk:RTK=5246...;URL=https://subdomain.zendesk.com;[email protected];Password=test123;InitiateOAuth=GETANDREFRESH;"

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.

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.

👁 Using the built-in connection string designer to generate a JDBC URL (Salesforce is shown.)

Load Zendesk Data

Once you configure the connection, you can load Zendesk data as a dataframe using the CData JDBC Driver and the connection information.

Step 2: Reading the data

remote_table = spark.read.format ( "jdbc" ) \
	.option ( "driver" , driver) \
	.option ( "url" , url) \
	.option ( "dbtable" , "Tickets") \
	.load ()

Display Zendesk Data

Check the loaded Zendesk data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("Id"))

👁 Displaying Zendesk Data

Analyze Zendesk Data in Databricks

If you want to process data with Databricks SparkSQL, register the loaded data as a Temp View.

Step 4: Create a view or table

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

With the Temp View created, you can use SparkSQL to retrieve the Zendesk data for reporting, visualization, and analysis.

% sql

SELECT Id, Subject FROM SAMPLE_VIEW ORDER BY Subject DESC LIMIT 5

👁 Displaying Zendesk Data

The data from Zendesk is only available in the target notebook. If you want to use it with other users, save it as a table.

remote_table.write.format ( "parquet" ) .saveAsTable ( "SAMPLE_TABLE" )

Download a free, 30-day trial of the CData JDBC Driver for Zendesk and start working with your live Zendesk data in Databricks. Reach out to our Support Team if you have any questions.