Process & Analyze Jira Service Management 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 Jira Service Management data. This article walks through hosting the CData JDBC Driver in AWS, as well as connecting to and processing live Jira Service Management data in Databricks.

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

Install the CData JDBC Driver in Databricks

To work with live Jira Service Management 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.jiraservicedesk.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

Access Jira Service Management Data in your Notebook: Python

With the JAR file installed, we are ready to work with live Jira Service Management 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 Jira Service Management, and create a basic report.

Configure the Connection to Jira Service Management

Connect to Jira Service Management by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the RTK 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.jiraservicedesk.JiraServiceDeskDriver"
url = "jdbc:jiraservicedesk:RTK=5246...;ApiKey=myApiKey;User=MyUser;InitiateOAuth=GETANDREFRESH"

Built-in Connection String Designer

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

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

You can establish a connection to any Jira Service Desk Cloud account or Server instance.

Connecting with a Cloud Account

To connect to a Cloud account, you'll first need to retrieve an APIToken. To generate one, log in to your Atlassian account and navigate to API tokens > Create API token. The generated token will be displayed.

Supply the following to connect to data:

• User: Set this to the username of the authenticating user.
• APIToken: Set this to the API token found previously.

Connecting with a Service Account

To authenticate with a service account, you will need to supply the following connection properties:

• User: Set this to the username of the authenticating user.
• Password: Set this to the password of the authenticating user.
• URL: Set this to the URL associated with your JIRA Service Desk endpoint. For example, https://yoursitename.atlassian.net.

Note: Password has been deprecated for connecting to a Cloud Account and is now used only to connect to a Server Instance.

Accessing Custom Fields

By default, the connector only surfaces system fields. To access the custom fields for Issues, set IncludeCustomFields.

Load Jira Service Management Data

Once you configure the connection, you can load Jira Service Management 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" , "Requests") \
	.load ()

Display Jira Service Management Data

Check the loaded Jira Service Management data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("RequestId"))

Analyze Jira Service Management 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 Jira Service Management data for reporting, visualization, and analysis.

% sql

SELECT RequestId, ReporterName FROM SAMPLE_VIEW ORDER BY ReporterName DESC LIMIT 5

The data from Jira Service Management 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 Jira Service Management and start working with your live Jira Service Management data in Databricks. Reach out to our Support Team if you have any questions.