Semantic link is a feature that allows you to establish a connection between [Power BI datasets](https://learn.microsoft.com/en-us/power-bi/connect-data/service-datasets-understand) and [Synapse Data Science in Microsoft Fabric](https://learn.microsoft.com/en-us/fabric/data-science/data-science-overview).
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.
[Package (PyPi)](https://pypi.org/project/semantic-link/) | [API reference documentation](https://learn.microsoft.com/en-us/python/api/semantic-link-sempy/) | [Product documentation](https://learn.microsoft.com/en-us/fabric/data-science/semantic-link-overview) | [Samples](https://github.com/microsoft/fabric-samples/tree/main/docs-samples/data-science/semantic-link-samples)
By downloading, installing, using or accessing this distribution package for semantic link, you agree to the [Terms of Service](https://github.com/microsoft/semantic-link-functions/blob/main/sempy/LICENSE.txt).
This package has been tested with Microsoft Fabric.
# Getting started
## Prerequisites
* A [Microsoft Fabric subscription](https://learn.microsoft.com/en-us/fabric/enterprise/licenses). Or sign up for a free [Microsoft Fabric (Preview) trial](https://learn.microsoft.com/en-us/fabric/get-started/fabric-trial).
* Sign in to [Microsoft Fabric](https://fabric.microsoft.com/).
* Create [a new notebook](https://learn.microsoft.com/en-us/fabric/data-engineering/how-to-use-notebook#create-notebooks) or a new [spark job](https://learn.microsoft.com/en-us/fabric/data-engineering/create-spark-job-definition) to use this package. **Note that semantic link is supported only within Microsoft Fabric.**
## About the semantic link packages
The functionalities for semantic link are split into multiple packages to allow for a modular installation.
If you want to install only a subset of the semantic link functionalities, you can install the individual packages instead of the `semantic-link` meta-package.
This can help solve dependency issues. The following are some of the available packages:
* [semantic-link](https://pypi.org/project/semantic-link/) - The meta-package that depends on all the individual semantic link packages and serves as a convenient way to install all the semantic link packages at once.
* [semantic-link-sempy](https://pypi.org/project/semantic-link-sempy/) - The package that contains the core semantic link functionality.
* [semantic-link-functions-holidays](https://pypi.org/project/semantic-link-functions-holidays/) - A package that contains semantic functions for holidays and dependence on [holidays](https://pypi.org/project/holidays).
* [semantic-link-functions-geopandas](https://pypi.org/project/semantic-link-functions-geopandas/) - A package that contains semantic functions for geospatial data and dependence on [geopandas](https://pypi.org/project/geopandas).
* ...
## Install the `semantic-link` meta package
To install the `semantic-link` package in Microsoft Fabric, you have two options:
* Install the `SemPy` Python library in your notebook kernel by executing this code in a notebook cell:
```python
%pip install semantic-link
```
* Alternatively, you can add semantic link to your workspace libraries directly. For more information, see [Install workspace libraries](https://learn.microsoft.com/en-us/fabric/data-science/python-guide/python-library-management#install-workspace-libraries).
# 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](https://github.com/microsoft/fabric-samples/tree/main/docs-samples/data-science/semantic-link-samples)
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