Azure Data Factory Management Client

azure-mgmt-datafactory is the Microsoft Azure Data Factory management client library for Python, part of the official Azure SDK for Python. It exposes the Data Factory control-plane API as a typed DataFactoryManagementClient, letting you programmatically create and manage factories along with their pipelines, datasets, linked services, triggers, integration runtimes, and data flows.

Generated from Azure’s REST API specifications, the package integrates with azure-identity for Microsoft Entra authentication and follows the standard Azure SDK design guidelines, offering both synchronous and asynchronous clients for automating Data Factory resources.

What You Get

• A typed DataFactoryManagementClient covering the full Data Factory control-plane API
• Operation groups for pipelines, datasets, linked services, triggers, and integration runtimes
• Synchronous and asyncio client variants under azure.mgmt.datafactory and its aio module
• Integration with azure-identity for Microsoft Entra authentication

Common Use Cases

• Automating creation and updates of Data Factory pipelines and datasets from Python
• Managing linked services and integration runtimes as part of infrastructure automation
• Triggering and monitoring pipeline and activity runs programmatically

Under The Hood

Architecture - The azure.mgmt.datafactory package centers on a generated DataFactoryManagementClient whose operations modules map to the service’s REST operation groups, with a parallel aio package for async, hundreds of models classes representing request/response bodies, and shared _utils for serialization; requests flow through the azure-core pipeline. Tech Stack - Pure Python 3.10+ built on azure-core and azure-mgmt-core, authenticated via azure-identity, and generated from TypeSpec/REST specs (see tsp-location.yaml) using Azure’s autorest-based code generator. Code Quality - As part of the azure-sdk-for-python monorepo, it inherits extensive CI, generated tests and samples (generated_tests, 100+ generated_samples), API-surface tracking (api.md), and consistent SDK-wide guidelines; the code is machine-generated and uniformly structured. API Design - It follows Azure’s standard management-plane conventions — credential plus subscription ID, then client.pipelines.create_or_update(...) style calls — so anyone familiar with other azure-mgmt packages transfers knowledge directly, at the cost of the verbosity typical of generated management SDKs.