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Semantic Link Labs is a Python library designed for use in Microsoft Fabric notebooks. This library extends the capabilities of Semantic Link offering additional functionalities to seamlessly integrate and work alongside it. The goal of Semantic Link Labs is to simplify technical processes, empowering people to focus on higher level activities and allowing tasks that are better suited for machines to be efficiently handled without human intervention.
Check out the helper notebooks for getting started!
Run the code below to load all the helper notebooks to the workspace of your choice at once.
import sempy_labs as labs
import requests
workspace_name = None # Update this to the workspace in which you want to save the notebooks
api_url = "https://api.github.com/repos/microsoft/semantic-link-labs/contents/notebooks"
response = requests.get(api_url)
files = response.json()
notebook_files = {file['name'][:-6]: file['html_url'] for file in files if file['name'].endswith('.ipynb')}
for file_name, file_url in notebook_files.items():
labs.import_notebook_from_web(notebook_name=file_name, url=file_url, workspace=workspace_name)
Install the library in a Fabric notebook
%pip install semantic-link-labs
Once installed, run this code to import the library into your notebook
import sempy_labs as labs
import sempy_labs.lakehouse as lake
import sempy_labs.report as rep
from sempy_labs import admin, deployment_pipeline, directlake, environment, eventstream, graph, graph_model, migration, mirrored_azure_databricks_catalog, ml_model, snowflake_database, sql_database, theme, variable_library, warehouse, semantic_model, eventhouse, dataflow
from sempy_labs.tom import connect_semantic_model
from sempy_labs.report import connect_report
An even better way to ensure the semantic-link-labs library is available in your workspace/notebooks is to load it as a library in a custom Fabric environment. If you do this, you will not have to run the above '%pip install' code every time in your notebook. Please follow the steps below.
Create a custom environment
Navigate to your Fabric workspace
Click 'New' -> More options
Within 'Data Science', click 'Environment'
Name your environment, click 'Create'
Add semantic-link-labs as a library to the environment
Within 'Public libraries', click 'Add from PyPI'
Enter 'semantic-link-labs'.
Click 'Save' at the top right of the screen
Click 'Publish' at the top right of the screen
Click 'Publish All'
Update your notebook to use the new environment (must wait for the environment to finish publishing)
Navigate to your Notebook
Select your newly created environment within the 'Environment' drop down in the navigation bar at the top of the notebook
The following process automates the migration of an import/DirectQuery model to a new Direct Lake model. The first step is specifically applicable to models which use Power Query to perform data transformations. If your model does not use Power Query, you must migrate the base tables used in your semantic model to a Fabric lakehouse.
[!NOTE]
The first 4 steps are only necessary if you have logic in Power Query. Otherwise, you will need to migrate your semantic model source tables to lakehouse tables.
The first step of the notebook creates a Power Query Template (.pqt) file which eases the migration of Power Query logic to Dataflows Gen2.
Navigate to your lakehouse (this is critical!). From your lakehouse, create a new Dataflows Gen2, and import the Power Query Template file. Doing this step from your lakehouse will automatically set the destination for all tables to this lakehouse (instead of having to manually map each one).
Publish the Dataflow Gen2 and wait for it to finish creating the delta lake tables in your lakehouse.
Back in the notebook, the next step will create your new Direct Lake semantic model with the name of your choice, taking all the relevant properties from the orignal semantic model and refreshing/framing your new semantic model.
[!NOTE]
Calculated tables are also migrated to Direct Lake (as data tables with their DAX expression stored as model annotations in the new semantic model). Additionally, Field Parameters are migrated as they were in the original semantic model (as a calculated table). Auto date/time tables are not migrated. Auto date/time must be disabled in Power BI Desktop and proper date table(s) must be created prior to migration.
Finally, you can easily rebind your all reports which use the import/DQ semantic model to the new Direct Lake semantic model in one click.
Completing these steps will do the following:
Offload your Power Query logic to Dataflows Gen2 inside of Fabric (where it can be maintained and development can continue).
Dataflows Gen2 will create delta tables in your Fabric lakehouse. These tables can then be used for your Direct Lake model.
Create a new semantic model in Direct Lake mode containing all the standard tables and columns, calculation groups, measures, relationships, hierarchies, roles, row level security, perspectives, and translations from your original semantic model.
Viable calculated tables are migrated to the new semantic model as data tables. Delta tables are dynamically generated in the lakehouse to support the Direct Lake model. The calculated table DAX logic is stored as model annotations in the new semantic model.
Field parameters are migrated to the new semantic model as they were in the original semantic model (as calculated tables). Any references to calculated columns which were not migrated are automatically removed in the new semantic model's field parameter(s).
Non-supported objects are not transferred (i.e. relationships using calculated columns or columns with unsupported data types etc.).
Reports used by your original semantic model will be rebinded to your new semantic model.
Limitations
Calculated columns in Direct Lake support the User Context expression context only (ExpressionContext = UserContext, requiring compatibility level 1705+). They are evaluated at query time (not materialized) and therefore cannot be used in relationships.
Migrating calculated columns requires a version of semantic-link-sempy which includes Microsoft.AnalysisServices v19.117.0 or later. Otherwise, calculated columns are not migrated.
Calculated columns within calculated tables are not migrated.
Auto date/time tables are not migrated.
References to calculated columns which are not migrated are removed from Field Parameters and will break in measure expressions or other DAX expressions.
Calculated tables are migrated as possible. The success of this migration depends on the interdependencies and complexity of the calculated table. This part of the migration is a workaround as technically calculated tables are not supported in Direct Lake.
See here for the rest of the limitations of Direct Lake.
Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.
A 'Create pull request' will appear at the top of the screen in green. Click it.
Enter details into the description.
Click 'Create'.
Code Formatting
We use black formatting as a code formatting standard. Make sure to run 'black' formatting on your code before submitting a pull request.
Run this code to install black
pip install black==25.1.0
Run this code to format your code using black
python -m black src
Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft
trademarks or logos is subject to and must follow
Microsoft's Trademark & Brand Guidelines.
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.
