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Python Connector Libraries for Azure DevOps Data Connectivity. Integrate Azure DevOps with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

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How to use SQLAlchemy ORM to access Azure DevOps Data in Python


Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Azure DevOps data.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for Azure DevOps and the SQLAlchemy toolkit, you can build Azure DevOps-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Azure DevOps data to query 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 CData Connector 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.

You can connect to your Azure DevOps account by providing the Organization and PersonalAccessToken.

Obtaining a Personal Access Token

A PersonalAccessToken is necessary for account authentication.

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.

Follow the procedure below to install SQLAlchemy and start accessing Azure DevOps through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit and SQLAlchemy ORM package:

pip install sqlalchemy pip install sqlalchemy.orm

Be sure to import the appropriate modules:

from sqlalchemy import create_engine, String, Column from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import sessionmaker

Model Azure DevOps Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Azure DevOps data.

NOTE: Users should URL encode the any connection string properties that include special characters. For more information, refer to the SQL Alchemy documentation.

engine = create_engine("azuredevops:///?AuthScheme=Basic&Organization=MyAzureDevOpsOrganization&ProjectId=MyProjectId&PersonalAccessToken=MyPAT&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for Azure DevOps Data

After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the Builds table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.

base = declarative_base() class Builds(base): __tablename__ = "Builds" Id = Column(String,primary_key=True) BuildNumber = Column(String) ...

Query Azure DevOps Data

With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.

Using the query Method

engine = create_engine("azuredevops:///?AuthScheme=Basic&Organization=MyAzureDevOpsOrganization&ProjectId=MyProjectId&PersonalAccessToken=MyPAT&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Builds).filter_by(Reason="Manual"): print("Id: ", instance.Id) print("BuildNumber: ", instance.BuildNumber) print("---------")

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

Builds_table = Builds.metadata.tables["Builds"] for instance in session.execute(Builds_table.select().where(Builds_table.c.Reason == "Manual")): print("Id: ", instance.Id) print("BuildNumber: ", instance.BuildNumber) print("---------")

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

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.