Fabric Semantic Models – EduDataSci – Educating the world about data and leadership

Semantic Models Aren’t the Finish Line: 10 Underused SemPy Functions for Fabric

If you’ve ever watched a team pour months into a semantic model—only to treat it as “the thing Power BI reads”—you’ve seen a common (and costly) mental model at work.

Semantic models shouldn’t be the last layer before visualization. In Microsoft Fabric, they can be a first-class part of the extended analytics and data science stack: something you can query, validate, profile, and even productize from notebooks. That shift is exactly what SemPy (the Python library behind Semantic Link) makes practical.

In this post, I’m going to do three things:

Introduce SemPy for Fabric as the bridge between semantic models and the rest of your Python workflows.
Share four “generic” functions that help you discover and understand a model’s surface area.
Highlight three functions that make consuming semantic models straightforward, and three that unlock capabilities you’d otherwise spend real time (and compute) rebuilding yourself.

Along the way, I’ll frame these as patterns for turning semantic models into data product surface areas—usable well beyond dashboards. This is where Microsoft Fabric and #SemPy start to feel less like “BI tooling” and more like part of your day-to-day analytics engineering and Data Science workflow.

The Hidden Permission Chain Behind Cross-Workspace Lakehouse Shortcuts (for Semantic Models)

One of the cleanest patterns in Microsoft Fabric is splitting your world in two: a “data product” workspace that owns curated lakehouses, and an “analytics” workspace that owns semantic models and reports. You connect the two with a OneLake shortcut, and suddenly you’ve avoided copies, reduced refresh complexity, and kept your architecture tidy.

Then the first DirectLake semantic model hits that shortcut and… the tables don’t load.

This post walks through what’s really happening in that moment in Microsoft Fabric, what permissions you actually need (and where), and how to tighten the whole pattern with OneLake Security instead of simply widening access. We’ll also cover the easy-to-miss caveat: if your shortcut ultimately lands on a Fabric SQL Database, you still have to do SQL permissions, too.

Edit, Retarget, and Redeploy: A Practical TMDL Folder Workflow for Fabric Semantic Models

There’s a moment in every Fabric semantic model lifecycle where the “click it in the UI” approach stops scaling.

It usually happens when you need to rename dozens (or hundreds) of fields to match a business glossary, or when Dev is stable and you’re ready to point the same model at a new Lakehouse for Test/Prod. That’s when the model stops being a diagram and starts being an artifact—something you want to treat like code.

This guide reflows the whole workflow end-to-end, using the Fabric service Edit in Desktop experience to open the model, exporting it to a PBIP project stored as a TMDL folder, editing that folder externally (no scripting inside Power BI Desktop), and then getting those changes back into the service—including the key capability you asked for:

retargeting the entire model to a different Lakehouse/Warehouse, and
retargeting a single table to a different physical table (even in a new Lakehouse).

We’ll do it with the mindset of Power BI + Microsoft Fabric development: repeatable changes, visible diffs, and fewer “hand edits” you regret later.