How to Build an ETL App for Azure DevOps 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 Azure DevOps and the petl framework, you can build Azure DevOps-connected applications and pipelines for extracting, transforming, and loading Azure DevOps data. This article shows how to connect to Azure DevOps with the CData Python Connector and use petl and pandas to extract, transform, and load Azure DevOps data.

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

Connecting to Azure DevOps Data

Connecting to Azure DevOps 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.

Obtaining a Personal Access Token

To generate one, log in to your Azure DevOps Organization account and navigate to Profile -> Personal Access Tokens -> New Token. The generated token will be displayed.

If you wish to authenticate to Azure DevOps using OAuth refer to the online Help documentation for an authentication guide.

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

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

cnxn = mod.connect("AuthScheme=Basic;Organization=MyAzureDevOpsOrganization;ProjectId=MyProjectId;PersonalAccessToken=MyPAT;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Azure DevOps

Use SQL to create a statement for querying Azure DevOps. In this article, we read data from the Builds entity.

sql = "SELECT Id, BuildNumber FROM Builds WHERE Reason = 'Manual'"

Extract, Transform, and Load the Azure DevOps Data

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

Loading Azure DevOps Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

cnxn = mod.connect("AuthScheme=Basic;Organization=MyAzureDevOpsOrganization;ProjectId=MyProjectId;PersonalAccessToken=MyPAT;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Id, BuildNumber FROM Builds WHERE Reason = 'Manual'"

table1 = etl.fromdb(cnxn,sql)

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

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