---
id: apache-airflow-providers-microsoft-fabric
version: "0.1.1"
license: unclear
license_treatment: unclear
maintenance: active
---

# apache-airflow-providers-microsoft-fabric — A plugin for Apache Airflow to interact with Microsoft Fabric items

License: unclear · Maintenance: active · Downloads: 5.9M/mo

## What it is and what it does

This is an Apache Airflow provider package that bridges Airflow DAGs to Microsoft Fabric, a unified analytics and data platform. It exposes operators to trigger and monitor Fabric jobs (notebooks, pipelines, Spark job definitions, DBT jobs, copy jobs, and materialized lake views) as well as Power BI semantic model refreshes. Authentication is handled via Service Principal or User Token methods, configured through Airflow connections.

The package includes three main operators: MSFabricRunJobOperator for triggering Fabric items with optional parameter passing and deferrable execution, MSFabricRunSemanticModelRefreshOperator for Power BI dataset refreshes, and MSFabricRunUserDataFunctionOperator for invoking User Data Functions. It also provides a secrets backend to resolve Fabric connection GUIDs to access tokens at runtime. The package depends only on requests and azure-identity, keeping installation overhead low.

Use it for:

- Schedule and monitor Fabric notebook executions from an Airflow DAG with custom parameters and timeout handling.
- Orchestrate multi-step Fabric data pipelines, triggering transformations and refreshes as part of a larger ETL workflow.
- Automate Power BI semantic model refreshes on a schedule or in response to upstream data events in Airflow.
- Chain Fabric jobs with other Airflow tasks to build end-to-end analytics workflows spanning multiple platforms.
- Invoke Fabric User Data Functions from Airflow with dynamic input parameters for real-time data processing.

## Worth the install?

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

Provides Apache Airflow operators to trigger, monitor, and orchestrate Microsoft Fabric jobs (notebooks, pipelines, Spark jobs) and semantic model refreshes from DAGs.

Yes, with conditions. Install if you run Airflow and need to orchestrate Microsoft Fabric jobs as part of your data pipeline. The low install friction and active maintenance are positive signals. However, verify the license is compatible with your project, confirm Alpha status does not block your use case, and test thoroughly in a non-production environment first. No known security vulnerabilities are reported.

## Install

pip install apache-airflow-providers-microsoft-fabric
uv add apache-airflow-providers-microsoft-fabric
poetry add apache-airflow-providers-microsoft-fabric

## Installing apache-airflow-providers-microsoft-fabric

Before you install: Low install friction with only two runtime dependencies (requests, azure-identity). Package is in active development with recent commits, though marked as Alpha status and still early in its release cycle.

License in practice: License treatment is unclear—no SPDX identifier or raw license text is available in the package metadata. Verify the actual license before adopting in a commercial or restricted-license context.

Quickstart:

pip install apache-airflow-providers-microsoft-fabric

from airflow.providers.microsoft.fabric.operators.run_item import MSFabricRunJobOperator

run_notebook = MSFabricRunJobOperator(
    task_id="run_fabric_notebook",
    workspace_id="<workspace_id>",
    item_id="<item_id>",
    fabric_conn_id="fabric_conn_id",
    job_type="RunNotebook",
    deferrable=True,
)

Requires Apache Airflow to be installed and configured; a Microsoft Fabric workspace and valid authentication credentials (Service Principal or User Token) must be set up in Airflow connections.

Verify before relying:

- Whether the unclear license is compatible with your project's license requirements—contact the maintainer or check the GitHub repository directly.
- Production readiness: Alpha status suggests the API may change; confirm stability expectations for your use case.
- Whether deferrable mode is fully supported across all operator types listed in the documentation.

## Package facts

- License: not declared (unclear)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 5.9M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

airflow microsoft fabric integration, orchestrate fabric notebooks from airflow, trigger fabric jobs airflow, fabric pipeline scheduling airflow, airflow fabric provider plugin, monitor fabric jobs airflow, fabric semantic model refresh airflow, airflow-provider, microsoft-fabric, orchestration

[View on SkillFed](https://skillfed.io/packages/apache-airflow-providers-microsoft-fabric) · [View on PyPI](https://pypi.org/project/apache-airflow-providers-microsoft-fabric/)