Forum Discussion
Advocate IPower BI dataflow scheduled refreshes sometimes fail with Execution Timeout Expired errors.
We have a couple of Power BI dataflows scheduled refreshes which sometimes fail when they have run over 10 minutes. The sources for the dataflow are Azure Synapes SQL pool and Office 365 Sharepoint sites. The below errors can be found in the log files.
DataSource.Error: Microsoft SQL: Execution Timeout Expired. The timeout period elapsed prior to completion of the operation or the server is not responding.
We've configured Incremental refresh and staggerd the schedules, but they still fail sometimes.
Is there a time out for 10 minutes setting for Power BI Dataflow Refresh somewhere? Can we increase the time out period?
The dataflow itself will time out in the preview window after 10min, and no way to change that, but if you save it it will validate and process.
You could try to break your dataflow into 2 stages.- Bring your Azure Synapse data into a dataflow with the other dataflow. No or minimal transformations.
- Create a 2nd dataflow that takes the tables from the first and does the big transformation. This will take advantage of the Enhanced Compute Engine and may process better. Doing everything in one dataflow when complex is usually discouraged.
3 Replies
- edhans
Community Champion
Is this a new thing, or has it been happening for a while? Are you on Premium Gen 2? There are known issues described on this page. Known issue - Long running, failed or stuck dataflow in Premium Gen2 - Power BI | Microsoft Docs
I am not aware of a way to increase the timeouts. 
KemeiThanks for the reply. It is from a new Power BI dataflow with the complex transformations that combine the data from Azure Synapse SQL pool and other Dataflow.
We are not sure where the 10 minutes timeout error comes from. Is it from Power BI services or Azure Synapse SQL pool?
- edhans
The dataflow itself will time out in the preview window after 10min, and no way to change that, but if you save it it will validate and process.
You could try to break your dataflow into 2 stages.- Bring your Azure Synapse data into a dataflow with the other dataflow. No or minimal transformations.
- Create a 2nd dataflow that takes the tables from the first and does the big transformation. This will take advantage of the Enhanced Compute Engine and may process better. Doing everything in one dataflow when complex is usually discouraged.