Vague Refresh Failure message: We're sorry, an error occurred during evaluation

[jakaihammuda]

Hi!

Has anyone expereinced this error message before, from a failed report refresh?

Our reports  are on a scheduled refresh, this error occured over the weekend, then on the next scheduled refresh, it was fine. No error. Its just very vague and we cannot see anything in our Databricks source side to suggest a cause for this...

Thanks!

Data source error: {"error":{"code":"DM_GWPipeline_Gateway_MashupDataAccessError","pbi.error":{"code":"DM_GWPipeline_Gateway_MashupDataAccessError","parameters":{},"details":[{"code":"DM_ErrorDetailNameCode_UnderlyingErrorCode","detail":{"type":1,"value":"-2147259"}},{"code":"DM_ErrorDetailNameCode_UnderlyingErrorMessage","detail":{"type":1,"value":"We're sorry, an error occurred during evaluation."}},{"code":"DM_ErrorDetailNameCode_UnderlyingHResult","detail":{"type":1,"value":"-214746"}}],"exceptionCulprit":1}}} 

[v-veshwara-msft]

Hi @jakaihammuda ,
We wanted to kindly follow up regarding your query. If you need any further assistance, please reach out.
Thank you.

[v-veshwara-msft]

Hi @jakaihammuda ,

Just checking in to see if you query is resolved and if any responses were helpful.
Otherwise, feel free to reach out for further assistance.

Thank you.

[grazitti_sapna]

Hi @jakaihammuda 

This error generally indicates a connectivity or evaluation timeout issue between Power BI Gateway and the Databricks source. Addressing gateway health, query optimization, privacy settings, and retry logic are the top approaches. Since the error is intermittent and resolved by the next scheduled refresh sometimes, transient network/service issues are likely at fault.

Quick Fixes:

1. Check Gateway & Network : Ensure the Power BI Gateway is online and can connect to your Databricks source without interruptions.
2. Verify Credentials and Privacy Settings : Double-check that the credentials used in Power BI Service for Databricks are correct and that the privacy level is set appropriately (usually “Organizational”).
3. Optimize Queries and Increase Timeouts: Review your queries for performance. If possible, increase any timeout settings to avoid premature refresh failures
4. Retry & Monitor Refreshes:  Since this error can be transient, monitor scheduled refreshes and manually retry if needed.

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[jakaihammuda]

Thanks @grazitti_sapna! Does either yourself or @rohit1991 know if it is possible to increased a timeout limit in the report or on Service for Databricks Serveless Warehouses? As i think our problem is with the fact that sometimes it can take up to 30 seconds for the warehouse to actually start

[grazitti_sapna]

Hi @jakaihammuda 

You can try the following steps to address the timeout limits in Power BI Service or within your report to see if it resolves the issue

• Power BI Service itself has a hard timeout limit of 2 hours per refresh for Pro licenses (longer for Premium)
• Within Power Query (Power BI Desktop), you can set a Command Timeout in minutes under Data Source settings → Change Source → Advanced Options → “Command Timeout in minutes (optional)”. This is respected during refresh in Power BI Desktop and often honored in Service but only up to the service limits
• However, this timeout setting is generic for the query execution and does not specifically handle the startup latency of the Databricks Serverless Warehouse

🌟 I hope this solution helps you unlock your Power BI potential! If you found it helpful, click 'Mark as Solution' to guide others toward the answers they need.

💡 Love the effort? Drop the kudos! Your appreciation fuels community spirit and innovation.

🎖 As a proud SuperUser and Microsoft Partner, we’re here to empower your data journey and the Power BI Community at large.

🔗 Curious to explore more? [Discover here].

Let’s keep building smarter solutions together!

[jakaihammuda]

@grazitti_sapna  - I am trying to check on this step you mentioned here:

• Within Power Query (Power BI Desktop), you can set a Command Timeout in minutes under Data Source settings → Change Source → Advanced Options → “Command Timeout in minutes (optional)”. This is respected during refresh in Power BI Desktop and often honored in Service but only up to the service limits
  
  However, 'Change Data Source' is greyed out for me - articles online arent too clear on how to enable. It points ot be any issue with the source step in PQ, but cant see why in my report...or what i need to change

[v-veshwara-msft · Accepted solution]

Hi @jakaihammuda ,

Thanks for checking on this further.

The “Change Data Source” option being greyed out usually means the connection is defined in the M code rather than exposed through the connector dialog, so the command timeout setting cannot be adjusted from the UI.

It is also important to note that the Command Timeout option is only supported in the SQL Server connector. For other connectors, including Databricks, the setting does not surface in the same way.

You can see a related discussion here: Timeout constantly when refreshing.

For Databricks, the practical options are to review query performance and, if needed, configure the STATEMENT_TIMEOUT  parameter on the Databricks side. Power BI Service will still enforce its own refresh limits.

Since the issue seems related to the startup time of the serverless warehouse, you may want to test with a provisioned SQL warehouse or keep the warehouse active to avoid the initial delay.

Hope this helps. Please reach out for further assistance.

Thank you.

[v-veshwara-msft]

Hi @jakaihammuda ,
Just wanted to check if the response provided was helpful. If further assistance is needed, please reach out.
Thank you.

[rohit1991]

Hi @jakaihammuda 

That message is the generic Mashup/Evaluation error. Since the next run succeeded, it was likely transient. Quick, practical checks:

• Timing/cluster: Databricks warehouse/cluster may have been asleep. Start it 5–10 mins earlier or shift the refresh time.

• Auth blip: Re-enter dataset creds (PAT/Entra) and confirm expiry.

• Capacity spike: If on Premium/Fabric, check Capacity Metrics around the failure time; avoid overlapping heavy refreshes.

• Find the culprit step: Refresh history >> View details to see which table/step failed; test that step in Desktop.

• Incremental refresh: Manually refresh the latest partition to isolate issues.

• Gateway (if used): Update gateway, check logs for that timestamp.

If it happens again, grab the Refresh ID and match it to Databricks query history for that minute this usually pinpoints the cause.

[jakaihammuda]

Thank you! Will take this on board and perform some checks 🙂