Forum Discussion
Issue on "Large dataset storage format" setting behavior in Power BI workspaces
Hi MAdam90 ,
Please try to consider using other methods:
(1)Using the enhanced refresh REST API , you can perform fine grained data refreshes, so that the memory needed by the refresh can be minimized to fit within your capacity's size.
(2) Optimize the data model by removing unnecessary columns, reducing the number of calculated columns, and using efficient data types. This helps to reduce the memory footprint of the dataset.
(3) Another option is to incrementally refresh the data. You can configure Power BI to refresh only a subset of data, such as the latest data or changed data, rather than refreshing the entire dataset. This helps reduce the memory requirements during the refresh process.Best Regards,
Neeko Tang
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
Hi MAdam90 ,
The error message you received about insufficient memory indicates that the refresh requires more memory than the capacity can fit.
It's possible that the "Small dataset" setting in your workspace is not the direct cause of the issue, but rather the memory limitations of your Power BI capacity.
In the meantime, you can try using the XMLA endpoint to refresh the new partitions one by one, instead of having multiple partitions being refreshed in parallel when the refresh is done by the Power BI Service. If refreshing one partition still gives the error, that means the capacity needs to be increased in size to fit the refresh.
Refer it :Large datasets in Power BI Premium - Power BI | Microsoft Learn
Best Regards,
Neeko Tang
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
hello Anonymous, XMLA endpoints for partitioning may not be applicable in my case, as my data is stored as CSV files in Azure, and CSV files don't natively support partitioning. XMLA is more suitable for Power BI datasets directly connected to a database.
Regards,
Hi MAdam90 ,
Please try to consider using other methods:
(1)Using the enhanced refresh REST API , you can perform fine grained data refreshes, so that the memory needed by the refresh can be minimized to fit within your capacity's size.
(2) Optimize the data model by removing unnecessary columns, reducing the number of calculated columns, and using efficient data types. This helps to reduce the memory footprint of the dataset.
(3) Another option is to incrementally refresh the data. You can configure Power BI to refresh only a subset of data, such as the latest data or changed data, rather than refreshing the entire dataset. This helps reduce the memory requirements during the refresh process.Best Regards,
Neeko Tang
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
