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Extract, Transform, and Load Jira Data in Python

The CData Python Connector for Jira enables you to create ETL applications and pipelines for Jira 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 and the petl framework, you can build Jira-connected applications and pipelines for extracting, transforming, and loading Jira data. This article shows how to connect to Jira with the CData Python Connector and use petl and pandas to extract, transform, and load Jira data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Jira data in Python. When you issue complex SQL queries from Jira, the driver pushes supported SQL operations, like filters and aggregations, directly to Jira and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Jira Data

Connecting to Jira 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.

To connect to JIRA, provide the User and Password. Additionally, provide the Url; for example, https://yoursitename.atlassian.net.

After installing the CData Jira Connector, follow the procedure below to install the other required modules and start accessing Jira 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 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.jira as mod

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

cnxn = mod.connect("User=admin;Password=123abc;Url=https://yoursitename.atlassian.net;")

Create a SQL Statement to Query Jira

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

sql = "SELECT Summary, TimeSpent FROM Issues WHERE ReporterDisplayName = 'Bob'"

Extract, Transform, and Load the Jira Data

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

Loading Jira Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData Python Connector for Jira, you can work with Jira 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 Python Connector to start building Python apps and scripts with connectivity to Jira 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.jira as mod

cnxn = mod.connect("User=admin;Password=123abc;Url=https://yoursitename.atlassian.net;")

sql = "SELECT Summary, TimeSpent FROM Issues WHERE ReporterDisplayName = 'Bob'"

table1 = etl.fromdb(cnxn,sql)

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

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