Semantic Model Refresh - Memory limit

Hi, p_da 

While dataset size is important, it's the memory required during refresh that counts. Even if your dataset is 40GB, it doesn't mean that Power BI will need the full 40GB of memory because it uses data compression. However, it still requires a lot of memory resources, especially during transformations and when working with complex queries or large tables in DirectQuery mode.
Reduce dataset size: Consider filtering your data to include only relevant periods (for example, focusing only on the last 12 months).
Incremental refresh: If applicable, implement incremental refresh to avoid loading the entire dataset each time. Incremental refresh only allows new or changed data to be loaded, reducing memory usage.
Complex calculated columns and measures can increase memory usage during refreshes. Simplify these calculations as much as possible or move them to a data source.
: For very large datasets, consider using the DirectQuery mode, which keeps the data in the source system and extracts the data only when needed. This can help avoid memory consumption during refreshes, but it also has other limitations (for example, reporting slower performance during interactions).

p_da 

I would suggest you to checkout this article to understand the memory consumption for data refresh operation 

https://blog.crossjoin.co.uk/2023/04/30/measuring-memory-and-cpu-usage-in-power-bi-during-dataset-refresh/

I addition to the hackcrr suggestion, I would like to suggest one more approach, you can use enhanced refresh operation keep the commit mode as 'partialBatch', it can reduce the memory consumption.

https://blog.crossjoin.co.uk/2024/05/12/the-partialbatch-commit-mode-in-the-power-bi-enhanced-refresh-api/

Please read the complete blog before testing. 

Need a Power BI Consultation? Hire me on Upwork

Connect on LinkedIn

Did I answer your question? Mark my post as a solution! If I helped you, click on the Thumbs Up to give Kudos. Proud to be a Super User!