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Extract, Transform, and Load Bugzilla Data in Python

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

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

Connecting to Bugzilla Data

Connecting to Bugzilla 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 authenticate to your Bugzilla account using two parameters:

  • URL: The URL of your Bugzilla developer's page (the Home page).
  • ApiKey: API Keys can be generated from the Preferences -> API Keys section of your Bugzilla developer's page.

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

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

cnxn = mod.connect("Url=http://yourdomain/Bugzilla;APIKey=abc123;")

Create a SQL Statement to Query Bugzilla

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

sql = "SELECT Id, Summary FROM Bugs WHERE Creator = 'user@domain.com'"

Extract, Transform, and Load the Bugzilla Data

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

Loading Bugzilla Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

cnxn = mod.connect("Url=http://yourdomain/Bugzilla;APIKey=abc123;")

sql = "SELECT Id, Summary FROM Bugs WHERE Creator = 'user@domain.com'"

table1 = etl.fromdb(cnxn,sql)

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

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