Microsoft Fabric Updates Blog

Semantic Link Updates June 2024 

We are excited to announce new and exciting updates to Semantic Link over the last a few months: 

  • Semantic Link is preinstalled in the default runtime
  • Semantic Link Labs is now live 
  • New change logs section is available 

Semantic Link is preinstalled in the default runtime 

At the Fabric Conference in March 2024, we announced that Semantic Link was generally available. We have now included Semantic Link in the default runtime by default. If you use Fabric with Spark 3.4 or later, semantic link is already in the default runtime, and you don’t need to install it.  

For Spark 3.3 or below, or to get the most recent version of Semantic Link, run this command: 

 %pip install -U semantic-link 

Semantic Link Labs is now live! 

Recently, Michael Kovalsky released a python library called ‘fabric_cat_tools‘ for Fabric notebooks.  This library includes 120+ additional functions which extend semantic-link’s capabilities ranging from automating the migration of semantic models to Direct Lake, analyzing semantic models via a Best Practice Analyzer, showing Vertipaq Analyzer statistics, wrapping the full Tabular Object Model…and much more. You can even automatically translate your entire semantic model’s metadata into any language in seconds! All of this is self-contained inside of the Fabric ecosystem. 

Figure 1: Using Semantic Link Labs to analyze semantic models via Vertipaq Analyzer.

We are excited to announce that this library has been renamed to Semantic Link Labs and open-sourced on Microsoft’s official GitHub page. As its name implies, semantic-link-labs is now an official extension of Semantic Link, offering early access to many features not yet available in Semantic Link but having the reassurance of a Microsoft-branded, open-sourced product.  

All functions within Semantic Link Labs are fully documented here.

We are confident Semantic Link Labs will help Power BI developers and admins easily automate previously complicated tasks as well as make semantic model optimization tooling more easily accessible within the Fabric ecosystem. The overwhelmingly positive feedback from this library (in its time as fabric_cat_tools) shows that semantic-link and Semantic Link Labs can offer a great deal to the Power BI community, not just the data science community.  We warmly welcome your contributions to our GitHub repository.

Figure 2: Using Semantic Link Labs to analyze semantic models via a Best Practice Analyzer.
Figure 3: Using Semantic Link Labs to automatically translate a semantic model’s metadata.

Access to the Tabular Object Model (TOM) 

You asked, we delivered! Using the connect_semantic_model function within Semantic Link Labs you get a convenient wrapper around the TOM server and 60+ functions for simplifying programmatic tasks for semantic modeling. If you want even more control, you can use the Semantic Link create_tom_server function. 

Here is a link to sample notebook for getting started.

with connect_semantic_model(dataset="My dataset", readonly=False, workspace="Your Workspace") as tom: 

tom.add_measure( 
table_name ='Internet Sales', 
measure_name = 'Sales Amount', 
expression = "SUM('Internet Sales'[SalesAmount])") 

New change log section is available 

We’ve also added a new “change logs” section to the semantic link · PyPI. It details everything included in each release. Check it out if you are curious about the latest updates and improvements on Semantic Link! 

Next Steps 

We hope that you enjoy the update! Please continue sending us your feedback and do not forget to vote for other features that you would like to see in Semantic Link! As always, keep voting on Ideas to help us determine what to build next. We look forward to hearing from you! 

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