microsoft/semantic-link-labs

Early access to new features for Microsoft Fabric's Semantic Link.

What it solves

Semantic Link Labs is a Python library that simplifies the management and optimization of data models, reports, and infrastructure within Microsoft Fabric. It automates technical tasks—such as migrating semantic models, analyzing model performance, and managing lakehouse tables—allowing users to focus on high-level data engineering and analysis rather than manual configuration.

How it works

The library extends the core Semantic Link functionality by providing a set of Python wrapper functions and tools. It integrates directly into Microsoft Fabric notebooks, allowing users to programmatically interact with Power BI, Fabric, Azure, and Microsoft Graph APIs to perform bulk updates, migrations, and system audits.

Who it’s for

It is designed for data engineers, Power BI developers, and Fabric administrators who work within the Microsoft Fabric ecosystem to manage complex semantic models and large-scale data environments.

Highlights

  • Semantic Model Management: Automates Direct Lake migration, bulk-generates measure descriptions, and provides tools for backup, restore, and deployment across workspaces.
  • Lakehouse Optimization: Includes capabilities to optimize and vacuum lakehouse tables and recover soft-deleted objects.
  • ** uma Report Analysis**: Features a Best Practice Analyzer (BPA) for reports and semantic models, and identifies broken visuals in Power BI reports.
  • Infrastructure Control: Allows for the programmatic creation, updating, and suspension of Fabric capacities.
  • API Integration: Provides simplified wrapper functions for Power BI, Fabric, Azure, and Microsoft Graph APIs.

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