adding Fabric notebook Python SQL created tables to Power Bi semantic model

[cindex-admin]

When will this issue be corrected?  adding Fabric notebook Python SQL created tables to Power Bi semantic model

[Anonymous]

Hi @cindex-admin,

Deploying data tables directly from Fabric Notebook to a Power BI semantic model is not supported at this time. Since the Notebook is a place to write code for data introduction, data preparation and data transformation, there needs to be a place to store the data after operations. We can usually use lakehouse or direct lake semantic model for undertaking. Depending on what you mentioned about wanting to lay out these data tables into the Power BI semantic model, you can do that by taking data from lakehouse. You can check out the following documentation for the steps to do this
How to use notebooks - Microsoft Fabric | Microsoft Learn
Load data into your lakehouse with a notebook - Microsoft Fabric | Microsoft Learn

Power Query Lakehouse (Beta) connector - Power Query | Microsoft Learn
Develop Direct Lake semantic models - Microsoft Fabric | Microsoft Learn

Best regards,
Albert He

If this post helps, then please consider Accept it as the solution to help the other members find it more quickly

[cindex-admin]

Mr He,

You answered with a shotgun, but you missed the target. I will provide you with more details on the specifics so you won't need to camouflage your answer.

Specific details:

Both accepted and non semantic model accepted tables have same properties. 

Properties: Name dailyuser Format delta  Type Managed URL  Relative path Tables/dailyuser

https://onelake.dfs.fabric.microsoft.com/ee03a3f0-e537-4cf1-af61-3522aa10229a/c762b569-85a9-4af2-b8a...

All are in same Lakehouse.

Accepted tables were pipelined over from Azure to Fabric Non-accepted were created by PySpark SQL(""" Insert, select from pipelined Azure to Fabric table""")

Here is the Explorer showing both accepted and non-accepted tables:

Both tables when viewing their Delta and ParQuet properties have the same attributes.

Now let's have some specific instructions on how to correct the issue.