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Get the Report →How to connect and process Jira Service Desk Data from Azure Databricks
Use CData, Azure, and Databricks to perform data engineering and data science on live Jira Service Desk Data
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 Desk data. This article walks through hosting the CData JDBC Driver in Azure, as well as connecting to and processing live Jira Service Desk data in Databricks.
With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live Jira Service Desk data. When you issue complex SQL queries to Jira Service Desk, the driver pushes supported SQL operations, like filters and aggregations, directly to Jira Service Desk 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 Desk data using native data types.
Install the CData JDBC Driver in Azure
To work with live Jira Service Desk data in Databricks, install the driver on your Azure cluster.
- Navigate to your Databricks administration screen and select the target cluster.
- On the Libraries tab, click "Install New."
- Select "Upload" as the Library Source and "Jar" as the Library Type.
- Upload the JDBC JAR file (cdata.jdbc.jiraservicedesk.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).
Connect to Jira Service Desk from Databricks
With the JAR file installed, we are ready to work with live Jira Service Desk 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 Desk, and create a basic report.
Configure the Connection to Jira Service Desk
Connect to Jira Service Desk by referencing the class for the JDBC Driver 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.
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 Desk JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.
java -jar cdata.jdbc.jiraservicedesk.jar
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 Desk Data
Once the connection is configured, you can load Jira Service Desk data as a dataframe using the CData JDBC Driver and the connection information.
remote_table = spark.read.format ( "jdbc" ) \ .option ( "driver" , driver) \ .option ( "url" , url) \ .option ( "dbtable" , "Requests") \ .load ()
Display Jira Service Desk Data
Check the loaded Jira Service Desk data by calling the display function.
display (remote_table.select ("RequestId"))
Analyze Jira Service Desk Data in Azure Databricks
If you want to process data with Databricks SparkSQL, register the loaded data as a Temp View.
remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )
The SparkSQL below retrieves the Jira Service Desk data for analysis.
% sql SELECT RequestId, ReporterName FROM Requests WHERE CurrentStatus = 'Open'
The data from Jira Service Desk 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 Desk and start working with your live Jira Service Desk data in Azure Databricks. Reach out to our Support Team if you have any questions.