search

microsoft / semantic-link-labs

Early access to new features for Microsoft Fabric's Semantic Link.
MIT License
176 stars 35 forks source link
analysis-services analysis-services-tabular dax jupyter-notebook microsoftfabric powerbi python

readme

Semantic Link Labs

PyPI version Read The Docs Code style: black Downloads

Read the documentation on ReadTheDocs!

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.

If you encounter any issues, please raise a bug.

If you have ideas for new features/functions, please request a feature.

Check out the video below for an introduction to Semantic Link, Semantic Link Labs and demos of key features!

Semantic Link Labs Video

Featured Scenarios

Helper Notebooks

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
from sempy_labs import migration, directlake, admin
from sempy_labs import lakehouse as lake
from sempy_labs import report as rep
from sempy_labs.tom import connect_semantic_model
from sempy_labs.report import ReportWrapper
from sempy_labs import ConnectWarehouse
from sempy_labs import ConnectLakehouse

Load Semantic Link Labs into a custom Fabric environment

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

  1. Navigate to your Fabric workspace
  2. Click 'New' -> More options
  3. Within 'Data Science', click 'Environment'
  4. Name your environment, click 'Create'

Add semantic-link-labs as a library to the environment

  1. Within 'Public libraries', click 'Add from PyPI'
  2. Enter 'semantic-link-labs'.
  3. Click 'Save' at the top right of the screen
  4. Click 'Publish' at the top right of the screen
  5. Click 'Publish All'

Update your notebook to use the new environment (must wait for the environment to finish publishing)

  1. Navigate to your Notebook
  2. Select your newly created environment within the 'Environment' drop down in the navigation bar at the top of the notebook

Version History

Direct Lake migration

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.

Check out Nikola Ilic's terrific blog post on this topic!

Check out my blog post on this topic!

Direct Lake Migration Video

Prerequisites

Instructions

  1. Download this notebook.
  2. Make sure you are in the 'Data Engineering' persona. Click the icon at the bottom left corner of your Workspace screen and select 'Data Engineering'
  3. In your workspace, select 'New -> Import notebook' and import the notebook from step 1.
  4. Add your lakehouse to your Fabric notebook
  5. Follow the instructions within the notebook.

The migration process

[!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.

  1. The first step of the notebook creates a Power Query Template (.pqt) file which eases the migration of Power Query logic to Dataflows Gen2.
  2. After the .pqt file is created, sync files from your OneLake file explorer
  3. 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).
  4. Publish the Dataflow Gen2 and wait for it to finish creating the delta lake tables in your lakehouse.
  5. 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.

  1. 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:

Limitations

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.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

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.