Microsoft Fabric  

Listing All Datasets Across Workspaces Using Semantic Link in Microsoft Fabric Notebooks

Microsoft Fabric is rapidly changing how data professionals interact with analytics assets, and Semantic Link is one of its most powerful (yet still underused) capabilities. If you’ve ever wanted to programmatically discover datasets (semantic models) across multiple workspaces , Fabric Notebooks combined with Semantic Link make this not only possible—but elegant.

In this article, we’ll walk through:

  • What Semantic Link is and why it matters

  • Why Fabric Notebooks are ideal for metadata exploration

  • How to list all datasets across workspaces using Semantic Link

  • Practical use cases for governance and automation

What Is Semantic Link in Microsoft Fabric?

Semantic Link is a Python-based capability in Microsoft Fabric that allows you to interact directly with Power BI semantic models (datasets) using code. Instead of relying solely on the UI, you can now:

  • Query semantic models using Python

  • Extract metadata programmatically

  • Integrate datasets into data science and engineering workflows

Under the hood, Semantic Link bridges Power BI, Fabric Lakehouses, and Notebooks , giving data engineers and analysts a single, unified experience.

Why Use Fabric Notebooks for This?

Fabric Notebooks are tightly integrated with the Fabric platform, which means:

  • Authentication is handled automatically

  • You don’t need to manage service principals or tokens manually

  • You can explore metadata across the tenant (subject to permissions)

This makes notebooks the perfect place for workspace and dataset discovery .

Listing All Datasets Across Workspaces

Using Semantic Link, we can query Fabric metadata to return all semantic models (datasets) that we have access to across workspaces.

Step 1: Import Semantic Link

import sempy.fabric as fabric

Step 2: List All Datasets

The list_datasets() function retrieves datasets across all accessible workspaces.

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This returns a Pandas DataFrame containing details such as:

  • Dataset ID

  • Dataset Name

  • Workspace ID

  • Workspace Name

  • Dataset Type (Semantic Model)

Step 2: List All Datasets in Different Workspace

To retrieve all the datasets in different workspace, execute:

# List all Datasets in different workspace
fabric.list_datasets(workspace="<workspace_id>")

Workspace ID can be found in your URL and it is after the group/

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Final Thoughts

Semantic Link turns Microsoft Fabric into more than just an analytics platform—it becomes a programmable data ecosystem. By combining Fabric Notebooks with Semantic Link, you can unlock deep visibility into your semantic layer and build automation that was previously impossible without complex APIs.

If you’re serious about data governance, automation, or platform observability in Fabric, this is a capability you should start using today.

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