Semantic link for Microsoft Fabric
Project description
Semantic Link is a feature that allows you to establish a connection between Power BI datasets and Synapse Data Science in Microsoft Fabric.
The primary goals of Semantic Link are to facilitate data connectivity, enable the propagation of semantic information, and seamlessly integrate with established tools used by data scientists, such as notebooks.
Semantic Link helps you to preserve domain knowledge about data semantics in a standardized way that can speed up data analysis and reduce errors.
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This package has been tested with Microsoft Fabric.
Getting started
Prerequisites
- A Microsoft Fabric subscription. Or sign up for a free Microsoft Fabric (Preview) trial.
- Sign in to Microsoft Fabric.
- Go to the Data Science experience in Microsoft Fabric.
- Create a new notebook to copy/paste code into cells.
Install the package
- Install the
SemPyPython library in your notebook kernel by executing this code in a notebook cell:
%pip install semantic-link
Please note that Semantic Link is split into multiple packages to allow for a modular installation.
If you want to install only a subset of the Semantic Link functionality, you can install the individual packages instead of the meta-package semantic-link.
This can help solve dependency issues.
The following packages are available:
- semantic-link-sempy - The core package that contains the Semantic Link functionality.
- semantic-link-functions-holidays - A package that contains semantic functions for holidays and dependence on holidays.
- semantic-link-functions-geopandas - A package that contains semantic functions for geospatial data and dependence on geopandas.
- ...
- semantic-link - A meta-package that depends on all the Semantic Link packages and serves as a convenient way to install all the Semantic Link packages.
Key concepts
SemPy offers the following capabilitites:
- Connectivity to Power BI
- Connectivity through Power BI Spark native connector
- Data augmentation with Power BI measures
- Semantic propagation for pandas users
- Built-in and custom semantic functions
Next steps
View our Samples
Release History
0.3.0 (2023-08-28)
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