If you’re refreshing a Power BI dataset that contains a large amount of data you may encounter the following error:
Data source error Unable to save the changes since the new dataset of size 1120 MB exceeds the limit of 1024 MB. If the dataset is hosted on a PowerBI Premium capacity, you may also reach out to your capacity administrator to see if the limit can be adjusted.
You’ll get this error at the end of a refresh if the size of your dataset is larger than the size allowed by Power BI. The maximum size of a dataset varies depending on whether you are using Shared capacity (commonly known as “Pro”) or Premium capacity:
- In Shared/Pro the maximum size of a dataset is 1GB (ie the 1024MB from the error message shown above), as documented here.
- In Premium:
- The default maximum size of a dataset is 10GB if the “Large dataset storage format” option (documented here and here) is turned off for your dataset.
- If the “Large dataset storage format” option is turned on, the maximum size of your dataset depends on the amount of memory available in the SKU of the Power BI Premium capacity you’re using. There’s a table in the documentation that tells you how much memory is available for each SKU: look in the “RAM per dataset” column and also note what it says in the footnotes about the maximum size of a dataset being slightly less than the amount of memory listed.
What can you do if you run into this error? You have two choices:
- If you’re using a Power BI Pro licence, buy Premium Per User licences or buy Premium capacity and move your dataset into a workspace on PPU or Premium. If you’re already on Premium, upgrade your capacity and make sure that the “Large dataset storage format” option is turned on for your dataset. This will increase the dataset size limits.
- Reduce the size of your dataset so it fits within the limits.
Option #2 is likely to be the preferred option in most cases. There are a lot of detailed blog posts and articles out there on how to reduce the size of your dataset (including this one in the docs) but basically you should use DAX Studio’s Model Metrics feature to see what’s taking up the most space in your dataset then delete unnecessary tables and columns, try reduce the number of distinct values in each column, and reduce the number of rows you’re loading into each table (perhaps by reducing the amount of historic data). Quite often a few small changes can significantly shrink the size of your dataset and also help refresh times and query performance.