Forum Discussion

HarderT's avatar
HarderT
Helper II
1 year ago
Solved

Error Data Warehouse/Lakehouse

Hey together,   since last week our Data Warehouse connection had errors:   "Message=<ccon>Warning: Fatal error 615 occurred at May 26 2025 8:08AM. Note the error and time, and contact your syste...
Lakehouse
  • Srisakthi's avatar
    Srisakthi
    1 year ago

    Hi HarderT ,

     

    You can utilise Semantic Link Labs to repoint to new lakehouse.

     

    import sempy.fabric as fabric
    from sempy_labs.directlake._get_shared_expression import get_shared_expression
    from sempy_labs._helper_functions import (
        resolve_lakehouse_name,
    )
    from sempy_labs.tom import connect_semantic_model
    from typing import Optional
    import sempy_labs._icons as icons


    def update_direct_lake_model_lakehouse_connection(
        dataset: str,
        workspace: Optional[str] = None,
        lakehouse: Optional[str] = None,
        lakehouse_workspace: Optional[str] = None,
    😞
        """
        Remaps a Direct Lake semantic model's SQL Endpoint connection to a new lakehouse.

        Parameters
        ----------
        dataset : str
            Name of the semantic model.
        workspace : str, default=None
            The Fabric workspace name in which the semantic model exists.
            Defaults to None which resolves to the workspace of the attached lakehouse
            or if no lakehouse attached, resolves to the workspace of the notebook.
        lakehouse : str, default=None
            The Fabric lakehouse used by the Direct Lake semantic model.
            Defaults to None which resolves to the lakehouse attached to the notebook.
        lakehouse_workspace : str, default=None
            The Fabric workspace used by the lakehouse.
            Defaults to None which resolves to the workspace of the attached lakehouse
            or if no lakehouse attached, resolves to the workspace of the notebook.

        Returns
        -------

        """

        workspace = fabric.resolve_workspace_name(workspace)

        if lakehouse_workspace is None:
            lakehouse_workspace = workspace

        if lakehouse is None:
            lakehouse_id = fabric.get_lakehouse_id()
            lakehouse = resolve_lakehouse_name(lakehouse_id, lakehouse_workspace)

        # Check if lakehouse is valid
        dfI = fabric.list_items(workspace=lakehouse_workspace, type="Lakehouse")
        dfI_filt = dfI[(dfI["Display Name"] == lakehouse)]

        if len(dfI_filt) == 0:
            raise ValueError(
                f"{icons.red_dot} The '{lakehouse}' lakehouse does not exist within the '{lakehouse_workspace}' workspace. "
                f"Therefore it cannot be used to support the '{dataset}' semantic model within the '{workspace}' workspace."
            )

        dfP = fabric.list_partitions(dataset=dataset, workspace=workspace)
        dfP_filt = dfP[dfP["Mode"] == "DirectLake"]

        if len(dfP_filt) == 0:
            raise ValueError(
                f"{icons.red_dot} The '{dataset}' semantic model is not in Direct Lake. This function is only applicable to Direct Lake semantic models."
            )
        else:
            with connect_semantic_model(
                dataset=dataset, readonly=False, workspace=workspace
            ) as tom:

                shEx = get_shared_expression(lakehouse, lakehouse_workspace)
                try:
                    tom.model.Expressions["DatabaseQuery"].Expression = shEx
                    print(
                        f"{icons.green_dot} The expression in the '{dataset}' semantic model has been updated to point to the '{lakehouse}' lakehouse in the '{lakehouse_workspace}' workspace."
                    )
                except Exception as e:
                    raise ValueError(
                        f"{icons.red_dot} The expression in the '{dataset}' semantic model was not updated."
                    ) from e
     
    Try the above code. Simalarly you can repoint the reports
     
    Regards,
    Srisakthi
     
    If this helps, please mark is Accept As Solution 
Anonymous's avatar
Anonymous
Not applicable
1 year ago

Hi HarderT,

 

As we haven’t heard back from you, we wanted to kindly follow up to check if the solution provided for the issue worked? or Let us know if you need any further assistance?
If our response addressed, please mark it as Accept as solution and click Yes if you found it helpful.

 

Regards,
Vinay Pabbu

  • HarderT's avatar
    HarderT
    Helper II
    1 year ago

    Hi Anonymous,

     

    the Problem now is when we change the connection it creates a new report and does not overwrite the old one.

    • Anonymous's avatar
      Anonymous
      Not applicable
      1 year ago

      Hi HarderT,

       

      Thats the Default behaviour. Power BI treats it as a new data source, which results in a new report being created instead of overwriting the existing one.
      if you're using Power BI Desktop, you can try updating the connection string from within the file and republish it, which should overwrite the existing report.

       

      Regards,

      Vinay Pabbu

    • Srisakthi's avatar
      Srisakthi
      Super User
      1 year ago

      Hi HarderT ,

       

      You can utilise Semantic Link Labs to repoint to new lakehouse.

       

      import sempy.fabric as fabric
      from sempy_labs.directlake._get_shared_expression import get_shared_expression
      from sempy_labs._helper_functions import (
          resolve_lakehouse_name,
      )
      from sempy_labs.tom import connect_semantic_model
      from typing import Optional
      import sempy_labs._icons as icons


      def update_direct_lake_model_lakehouse_connection(
          dataset: str,
          workspace: Optional[str] = None,
          lakehouse: Optional[str] = None,
          lakehouse_workspace: Optional[str] = None,
      😞
          """
          Remaps a Direct Lake semantic model's SQL Endpoint connection to a new lakehouse.

          Parameters
          ----------
          dataset : str
              Name of the semantic model.
          workspace : str, default=None
              The Fabric workspace name in which the semantic model exists.
              Defaults to None which resolves to the workspace of the attached lakehouse
              or if no lakehouse attached, resolves to the workspace of the notebook.
          lakehouse : str, default=None
              The Fabric lakehouse used by the Direct Lake semantic model.
              Defaults to None which resolves to the lakehouse attached to the notebook.
          lakehouse_workspace : str, default=None
              The Fabric workspace used by the lakehouse.
              Defaults to None which resolves to the workspace of the attached lakehouse
              or if no lakehouse attached, resolves to the workspace of the notebook.

          Returns
          -------

          """

          workspace = fabric.resolve_workspace_name(workspace)

          if lakehouse_workspace is None:
              lakehouse_workspace = workspace

          if lakehouse is None:
              lakehouse_id = fabric.get_lakehouse_id()
              lakehouse = resolve_lakehouse_name(lakehouse_id, lakehouse_workspace)

          # Check if lakehouse is valid
          dfI = fabric.list_items(workspace=lakehouse_workspace, type="Lakehouse")
          dfI_filt = dfI[(dfI["Display Name"] == lakehouse)]

          if len(dfI_filt) == 0:
              raise ValueError(
                  f"{icons.red_dot} The '{lakehouse}' lakehouse does not exist within the '{lakehouse_workspace}' workspace. "
                  f"Therefore it cannot be used to support the '{dataset}' semantic model within the '{workspace}' workspace."
              )

          dfP = fabric.list_partitions(dataset=dataset, workspace=workspace)
          dfP_filt = dfP[dfP["Mode"] == "DirectLake"]

          if len(dfP_filt) == 0:
              raise ValueError(
                  f"{icons.red_dot} The '{dataset}' semantic model is not in Direct Lake. This function is only applicable to Direct Lake semantic models."
              )
          else:
              with connect_semantic_model(
                  dataset=dataset, readonly=False, workspace=workspace
              ) as tom:

                  shEx = get_shared_expression(lakehouse, lakehouse_workspace)
                  try:
                      tom.model.Expressions["DatabaseQuery"].Expression = shEx
                      print(
                          f"{icons.green_dot} The expression in the '{dataset}' semantic model has been updated to point to the '{lakehouse}' lakehouse in the '{lakehouse_workspace}' workspace."
                      )
                  except Exception as e:
                      raise ValueError(
                          f"{icons.red_dot} The expression in the '{dataset}' semantic model was not updated."
                      ) from e
       
      Try the above code. Simalarly you can repoint the reports
       
      Regards,
      Srisakthi
       
      If this helps, please mark is Accept As Solution 
  • Anonymous's avatar
    Anonymous
    Not applicable
    1 year ago

    Hi @HarderT,

     

    As we haven’t heard back from you, we wanted to kindly follow up to check if the solution provided for the issue worked? or Let us know if you need any further assistance?
    If our response addressed, please mark it as Accept as solution and click Yes if you found it helpful.

     

    Regards,
    Vinay Pabbu