Fabric

The next figure shows how Semantic Link creates a bridge between data science and Business Intelligence by providing the possibility to leverage semantic model data in data science using a familiar set of tools: Notebooks, Python, and Spark, to name a few of the most popular.

This library extends the generic sempy library and consists of multiple ready-made functions you can use out-of-the-box to leverage Semantic Link feature in Microsoft Fabric

Writing Python code is not something you would expect from every data professional. Hence, the credit for the ease of use of various Semantic Link implementations

The following figure illustrates a dependency tree of the FactInternetSales table from the sample Adventure Works database. I was able to plot the dependency tree by executing literally six lines of Python code.

Despite all of the other data science and machine learning tools and features, such as MLFlow, SynapseML, and AutoML, I sincerely consider Semantic Link

s. I firmly believe that what was a missing link before, now with Semantic Link becomes the strongest link, as this feature finally enables bridging the gap between data science and Business Intelligence.

If you take a look at the official documentation, you’ll learn that “Semantic link is a feature that allows you to establish a connection between semantic models and Synapse Data Science in Microsoft Fabric”. Doesn’t sound too exciting, right?

Using Semantic-Link-Labs to build the solution The good news is that Michael Kovalsky from elegantbi.com has created the Semantic-Link-Labs which allows this to be achieved using a Notebook. I first installed the Semantic-Link-Labs