Pushed semantic model is not listed in dataset list while using semantic-link-sempy Python package

Hi Karel ,

Firstly, it's important to understand that pushed semantic models in Power BI have specific characteristics and limitations. As you've observed, these models are designed primarily for near real-time streaming scenarios and are limited in their functionality compared to other types of datasets. Specifically, pushed semantic models are not intended for extensive custom report creation or editing beyond adding new measures. This is due to the nature of how data is stored and managed within these models. For more detailed information on the limitations and intended use of pushed semantic models, you can refer to the Push Datasets Limitations documentation.

Regarding the issue with fabric.list_datasets() not listing your pushed semantic model, this behavior aligns with the limitations of pushed semantic models. These models are managed differently within the Power BI service, which can affect how they are accessed and manipulated through external tools and APIs.

To work around this limitation and achieve your goal of adapting pushed tables and adding calculated columns, you might consider the following approach:

1. Export Data Programmatically: Instead of directly accessing the pushed semantic model, you could export the data programmatically from Power BI to a storage solution like Azure Blob Storage or Azure Data Lake.
2. Create a New Semantic Model: Use the exported data to create a new semantic model in Power BI Desktop or the Power BI service, where you have more flexibility to add calculated columns and perform other manipulations.
3. Automate Data Refresh: Set up an automated process to periodically refresh the data in your new semantic model, simulating a near real-time data flow.
   This approach allows you to bypass the limitations of pushed semantic models while still leveraging the real-time data they provide.

Best regards,
Community Support Team_Binbin Yu
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