{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis"}],"enrichment":{"capability":"Connects Python notebooks and Spark jobs in Microsoft Fabric to Power BI datasets, enabling data scientists to query semantic models, augment data with Power BI measures, and propagate semantic information across analysis workflows.","skillfed_tags":["fabric-integration","power-bi","admin-api"],"use_cases":["Query Power BI semantic models and DAX measures directly from a Fabric notebook to augment pandas or Spark DataFrames with business logic.","Automate tenant and workspace administration tasks such as listing workspaces, managing user access, and assigning capacities programmatically.","Deploy or update semantic model definitions across Fabric workspaces, including remapping Direct Lake connections to new lakehouses.","Extract and modify Power BI report layouts by reading and updating report.json files within a Fabric notebook workflow.","Propagate semantic metadata and domain knowledge across data science projects to standardize analysis and reduce errors."],"what_it_does":"Semantic-link-sempy is the core package of Microsoft's semantic link feature, designed to bridge Power BI datasets and Microsoft Fabric's data science environment. It provides Python APIs to connect Spark notebooks and jobs to Power BI semantic models, query them via DAX, and enrich data analysis with Power BI measures and domain semantics. The package is built for data scientists working in Fabric who need to access and leverage semantic information stored in Power BI without leaving their notebooks.\n\nThe package includes connectivity to Power BI through the Spark native connector, data augmentation capabilities, and a comprehensive admin API (75 functions) for managing Fabric workspaces, capacities, domains, datasets, reports, and tenant settings. It depends on a large ecosystem of Azure SDKs, PySpark, pandas, and IPython, reflecting its tight integration with the Fabric platform. The package is classified as Beta, indicating active development and potential API changes.","worth_installing":"Yes, if you are actively working in Microsoft Fabric and need to integrate Power BI semantic models into your data science workflows. The package is actively maintained, has low install friction, and provides essential connectivity for Fabric-native development. However, the proprietary license and Beta status mean you should review Microsoft's terms and test thoroughly before production use. Not suitable for standalone Python environments or non-Fabric contexts."},"id":"semantic-link-sempy","links":{"html":"https://skillfed.io/packages/semantic-link-sempy","md":"https://skillfed.io/packages/semantic-link-sempy.md","pypi":"https://pypi.org/project/semantic-link-sempy/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-21","license_spdx":null,"license_treatment":"unclear","name":"semantic-link-sempy","python_support":"supports_current","summary":"Semantic link for Microsoft Fabric"},"popularity":{"monthly_downloads":951817,"position":4654,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.14.2"}