{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/4"}],"enrichment":{"capability":"Provides Apache Airflow operators to trigger, monitor, and orchestrate Microsoft Fabric jobs (notebooks, pipelines, Spark jobs) and semantic model refreshes from DAGs.","skillfed_tags":["airflow-provider","microsoft-fabric","orchestration"],"use_cases":["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."],"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.\n\nThe 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.","worth_installing":"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."},"id":"apache-airflow-providers-microsoft-fabric","links":{"html":"https://skillfed.io/packages/apache-airflow-providers-microsoft-fabric","md":"https://skillfed.io/packages/apache-airflow-providers-microsoft-fabric.md","pypi":"https://pypi.org/project/apache-airflow-providers-microsoft-fabric/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-04-25","license_spdx":null,"license_treatment":"unclear","name":"apache-airflow-providers-microsoft-fabric","python_support":"supports_current","summary":"A plugin for Apache Airflow to interact with Microsoft Fabric items"},"popularity":{"monthly_downloads":5929909,"position":2009,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.1.1"}