---
id: azure-mgmt-datafactory
version: "10.0.0"
license: MIT
license_treatment: permissive
maintenance: active
---

# azure-mgmt-datafactory — Microsoft Azure Datafactory Management Client Library for Python

License: permissive · Maintenance: active · Downloads: 13.3M/mo

## What it is and what it does

This is the official Python client library for managing Azure Data Factory resources through the Azure Resource Manager API. It provides programmatic access to create, read, update, and delete Data Factory components—including factories, pipelines, datasets, linked services, triggers, integration runtimes, and related entities—without using the Azure Portal or CLI.

The library handles authentication via Microsoft Entra and abstracts the REST API layer. Version 10.0.0 introduces system_data tracking across resource models and transitions to keyword-only parameters for conditional operations (etag/match_condition), marking a significant API evolution. It requires Python 3.10 or later and depends on isodate, azure-mgmt-core, and typing-extensions for core functionality.

Use it for:

- Automate provisioning and configuration of Data Factory pipelines and linked services in CI/CD workflows.
- Programmatically create and manage datasets and data flows for ETL/ELT processes at scale.
- Monitor and update integration runtimes and trigger schedules without manual portal access.
- Build custom orchestration tools that interact with Data Factory resources as part of larger data platform automation.
- Retrieve metadata and system data from Data Factory resources for auditing and compliance reporting.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Manages Azure Data Factory resources—factories, pipelines, datasets, linked services, triggers, and integration runtimes—via the Azure Management API.

Yes. This is the official, actively maintained Azure SDK library for Data Factory management. Install friction is low, dependencies are minimal and stable, no known vulnerabilities exist, and the MIT license poses no restrictions. Use it when you need programmatic control over Data Factory resources; avoid it only if you have no Azure Data Factory workloads or prefer portal/CLI-only management.

## Install

pip install azure-mgmt-datafactory
uv add azure-mgmt-datafactory
poetry add azure-mgmt-datafactory

## Installing azure-mgmt-datafactory

Before you install: Low install friction; pure Python wheel with only three runtime dependencies (isodate, azure-mgmt-core, typing-extensions). Actively maintained with a recent release (37 days old) and ongoing repository activity.

License in practice: MIT license permits commercial and private use with minimal restrictions; suitable for most projects.

Quickstart:

pip install azure-mgmt-datafactory

from azure.mgmt.datafactory import DataFactoryManagementClient
from azure.identity import DefaultAzureCredential
import os

sub_id = os.getenv("AZURE_SUBSCRIPTION_ID")
client = DataFactoryManagementClient(credential=DefaultAzureCredential(), subscription_id=sub_id)

Requires Python 3.10 or later. Azure authentication credentials (AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET, AZURE_SUBSCRIPTION_ID) must be configured as environment variables.

Verify before relying:

- Whether version 10.0.0's breaking changes to method signatures (etag/match_condition parameters) affect existing codebases.
- Performance characteristics and rate limits when managing large numbers of Data Factory resources.
- Specific authentication flow details beyond environment variable configuration.

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 13.3M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

azure data factory management, azure pipeline automation, azure etl orchestration, azure data integration, azure factory client library, azure resource management, data factory api python, azure-sdk, cloud-management, etl-orchestration

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