Find Unused Objects In Power BI Semantic Models With Semantic Link Labs

A few weeks ago I wrote about how Semantic Link Labs now has tools with interactive UIs and showed how you could use this to view lineage and so Vertipaq-Analyzer-stuff in a notebook. I didn’t show what I think is the coolest new feature though: the ability to find the tables, columns and measures in a semantic model that aren’t used and which therefore could be deleted. There are tons of excellent third-party tools that do this available already of course but the advantage of using Semantic Link Labs for this is that you can automate the process of finding unused columns and run it from a notebook and, crucially, you have two ways of finding those unused columns: from analysing the structure of downstream reports and from analysing DAX queries captured in Workspace Monitoring.

To test this I used a simple semantic model with one table in a workspace with Workspace Monitoring enabled:

I then created a report with a single visual showing the Person Count measure broken down by gender:

Next I created a notebook with the following code that uses find_unused_objects:

%pip install semantic-link-labs
import sempy_labs as labs
labs.semantic_model.find_unused_objects(
    dataset = "insertsemanticmodelidhere",
    workspace = "insertworkspaceidhere",
    method = "WorkspaceMonitoring",
    visualize=True
)

This displayed the following widget which queried Workspace Monitoring and found three DAX queries in the previous seven hours:

Clicking the Analyze button showed me – as expected – that only the Gender and Person Count measures had been used in these three queries and that all the rest of the columns were unused:

Of course you don’t need to use the widget and can get a pandas dataframe back instead.