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haqv's avatar
haqv
New Member
1 year ago
Solved

Schedules/On-demand refresh failed

Hello Power Bi team,   I had a Pbi report with size of 800mb. Data is connected to PBI report via sharepoint folder gateway. My data is about 3Gb-5Gb for 3-year transactions. I publised my report t...
Need Help
  • tharunkumarRTK's avatar
    tharunkumarRTK
    1 year ago

    haqv 

    Incremental refresh with sharepoint or onedrive is not feasible though there are few workarounds, pelase follow the instructions in this video and see if there is anything wrong in the implementation 

    https://www.youtube.com/watch?v=6Uy59RsqgrM

     

    I would suggest you not to perform the full load during the initial refresh, rather perform the bootstraped refresh i.e. truncate the dataset and perform the first load. This will create empty partitions in the power bi service. Then refresh the partitions one after the other. Instructions are here

    https://learn.microsoft.com/en-us/power-bi/connect-data/incremental-refresh-xmla#prevent-timeouts-on-initial-full-refresh

     

    Considering you are using data gateway, and your sources are excel files, make sure your gateway server has sufficient resources to perform the data refresh.

     

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Anonymous's avatar
Anonymous
Not applicable
1 year ago

Hi haqv,

 

Thank you for reaching out to the Microsoft Fabric Forum Community.

and also thanks to ribisht17  and Deku and tharunkumarRTK for prompt and helpful responses.

 

Your Power BI report's first refresh is slow or not completing in the service because the initial incremental refresh performs a full data load, which is heavy due to your 3–5 GB dataset from a SharePoint folder a source not optimized for large scale refreshes. To resolve this, reduce the initial dataset size by adjusting the RangeStart and RangeEnd parameters (e.g., to only 6 months of data), publish, and perform the first refresh. Once successful, expand the range back to cover the full 3 years. Additionally, optimize your Power Query steps by filtering early, removing unnecessary columns, and avoiding complex transformations before file expansion. If possible, switch to a more efficient data source like OneDrive or Azure Data Lake, or use Power BI Dataflows to stage the data. These steps should improve refresh performance on the Power BI Service under your Premium Per User license.

If you find this response helpful, please consider marking it as the accepted solution and giving it a thumbs-up to support others in the community.


Thank you & regards,
Prasanna Kumar