azure-mgmt-datafactory
Microsoft Azure Datafactory Management Client Library for Python
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- 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.
- Low install friction; pure Python wheel with only three runtime dependencies (isodate, azure-mgmt-core, typing-extensions).
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions; suitable for most projects.
last release 2026-07-08 (37 days) · last repo commit 2026-08-14 · 5,588 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 13,290,909 downloads/mo, #1,289 on PyPI
Alternatives
Verify before relying
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)- 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.
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 on this page. Verify before relying on it.
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
azure-mgmt-datafactory on PyPI
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.
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.
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)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 the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesisodateazure-mgmt-coretyping-extensions |
| Maintenance | Actively maintained 37 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 13,290,909 / month, #1,289 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: azure_mgmt_datafactory-10.0.0-py3-none-any.whl
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See also azure-mgmt-resource · azure-mgmt-loganalytics · azure-mgmt-kusto · azure-mgmt-batch · azure-synapse-artifacts · azure-mgmt-network · azure-mgmt-managementgroups · azure-mgmt-storage · azure-mgmt-databricks · azure-mgmt-resourcehealth