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Extract, Transform, and Load Jira Service Desk Data in Python

The CData Python Connector for Jira Service Desk enables you to create ETL applications and pipelines for Jira Service Desk data in Python with petl.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Jira Service Desk and the petl framework, you can build Jira Service Desk-connected applications and pipelines for extracting, transforming, and loading Jira Service Desk data. This article shows how to connect to Jira Service Desk with the CData Python Connector and use petl and pandas to extract, transform, and load Jira Service Desk data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Jira Service Desk data in Python. When you issue complex SQL queries from 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).

Connecting to Jira Service Desk Data

Connecting to Jira Service Desk data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

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.

After installing the CData Jira Service Desk Connector, follow the procedure below to install the other required modules and start accessing Jira Service Desk through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for Jira Service Desk Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.jiraservicedesk as mod

You can now connect with a connection string. Use the connect function for the CData Jira Service Desk Connector to create a connection for working with Jira Service Desk data.

cnxn = mod.connect("ApiKey=myApiKey;User=MyUser;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Jira Service Desk

Use SQL to create a statement for querying Jira Service Desk. In this article, we read data from the Requests entity.

sql = "SELECT RequestId, ReporterName FROM Requests WHERE CurrentStatus = 'Open'"

Extract, Transform, and Load the Jira Service Desk Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Jira Service Desk data. In this example, we extract Jira Service Desk data, sort the data by the ReporterName column, and load the data into a CSV file.

Loading Jira Service Desk Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'ReporterName')

etl.tocsv(table2,'requests_data.csv')

In the following example, we add new rows to the Requests table.

Adding New Rows to Jira Service Desk

table1 = [ ['RequestId','ReporterName'], ['NewRequestId1','NewReporterName1'], ['NewRequestId2','NewReporterName2'], ['NewRequestId3','NewReporterName3'] ]

etl.appenddb(table1, cnxn, 'Requests')

With the CData Python Connector for Jira Service Desk, you can work with Jira Service Desk data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

Download a free, 30-day trial of the Jira Service Desk Python Connector to start building Python apps and scripts with connectivity to Jira Service Desk data. Reach out to our Support Team if you have any questions.



Full Source Code


import petl as etl
import pandas as pd
import cdata.jiraservicedesk as mod

cnxn = mod.connect("ApiKey=myApiKey;User=MyUser;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT RequestId, ReporterName FROM Requests WHERE CurrentStatus = 'Open'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'ReporterName')

etl.tocsv(table2,'requests_data.csv')

table3 = [ ['RequestId','ReporterName'], ['NewRequestId1','NewReporterName1'], ['NewRequestId2','NewReporterName2'], ['NewRequestId3','NewReporterName3'] ]

etl.appenddb(table3, cnxn, 'Requests')