Need help with error: The XML for Analysis request timed out before it was completed

Hi Sunflower7500 

It appears that the error could be coming from the databricks connection, and potentially not from the analysis services request. This is because the analysis services request is sending this through two databricks. Could you maybe check on your data warehouse site to see when the query is running and how long it takes to return the information?

Hi Sunflower7500,
Thank for posting your query in Microsoft fabric community.

we understand that your Power BI dataset refresh is timing out intermittently, even though it works in Power BI Desktop. This can happen due to query performance, service load, or timeout settings. I would recommend you the below steps:

• Try refreshing at a different time (off-peak hours) to see if it completes successfully.
• Please optimize your Databricks query by ensuring proper indexing, minimizing transformations, and verifying cluster performance.
• If your workspace is in Power BI Premium, you may be able to adjust timeout settings via XMLA endpoints.
• Check Dataset Settings → Refresh History for any error details.
• If applicable, incremental refresh can help improve performance.

Please refer the below Microsoft Official links:
Data refresh in Power BI - Power BI | Microsoft Learn
Troubleshoot refresh scenarios - Power BI | Microsoft Learn

Please refer to the following similar thread link:
Timeout Value in Power BI Service

If this helps, then please Accept it as a solution and dropping a "Kudos" so other members can find it more easily.
Thank you.

Hi Sunflower7500 

The "XML for Analysis request timed out before it was completed" error in Power BI Service suggests that the dataset refresh is exceeding the 7200-second (2-hour) limit despite working fine in Power BI Desktop. Since your PBIX file is small (598 KB) and refreshes within an hour manually, the issue is likely due to performance fluctuations in Databricks or Power BI Service capacity constraints. The error occurs intermittently, which suggests that Databricks may be experiencing high load at certain times, leading to slower query execution. To resolve this, you can optimize your query in Databricks, enable query folding, or consider incremental refresh to reduce processing time. Additionally, adjusting the refresh schedule to avoid peak usage times and checking for resource contention in Power BI shared capacity can help improve reliability. If the issue persists, moving to a dedicated capacity (Premium/Fabric) or tuning Databricks performance settings may be necessary. Monitoring query execution times in Databricks and analyzing Power BI Service refresh logs can provide further insights into the root cause.