How to Build an ETL App for Jira Assets Data in Python with CData

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

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

Connecting to Jira Assets Data

Connecting to Jira Assets 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.

Jira Assets supports connecting and authenticating via the APIToken.

To generate an API token:

1. Log in to your Atlassian account.
2. Navigate to Security < Create and manage API Token < Create API Token.

Atlassian generates and then displays the API token.

After you have generated the API token, set these parameters:

• AuthScheme: APIToken.
• User: The login of the authenticating user.
• APIToken: The API token you just generated.

You are now ready to connect and authenticate to Jira Assets.

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

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

cnxn = mod.connect("User=MyUser;APIToken=myApiToken;Url=https://yoursitename.atlassian.net")

Create a SQL Statement to Query Jira Assets

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

sql = "SELECT ID, Name FROM Objects WHERE Label = 'SYD-1'"

Extract, Transform, and Load the Jira Assets Data

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

Loading Jira Assets Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Jira Assets

table1 = [ ['ID','Name'], ['NewID1','NewName1'], ['NewID2','NewName2'], ['NewID3','NewName3'] ]

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

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

cnxn = mod.connect("User=MyUser;APIToken=myApiToken;Url=https://yoursitename.atlassian.net")

sql = "SELECT ID, Name FROM Objects WHERE Label = 'SYD-1'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['ID','Name'], ['NewID1','NewName1'], ['NewID2','NewName2'], ['NewID3','NewName3'] ]

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