Forum Discussion
Timeout error when refreshing semantic model connected to dataverse
Hi shimgarah ,
Since the issue started recently and used to work fine before, it may be tied to backend performance changes, query complexity, or API throttling on the Dataverse side.Could you please try the following steps :
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Review Query Folding
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In Power Query, right-click a query step and choose "View Native Query" if this option is greyed out, your query isn’t folding, which can severely impact performance.
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Try moving filters, column removals, and joins as early as possible in the query to encourage folding.
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Split Large Tables
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If one table is the bottleneck, try splitting it into two queries (e.g., current year + historical), and import them separately. You can merge or append them later if needed.
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Use Incremental Refresh (If on Premium/Pro + Service)
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Set up incremental refresh so that Power BI only loads changed data rather than reloading everything every time.
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Monitor Dataverse Limits
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Dataverse enforces API call limits and performance throttling.
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Try monitoring request load through Power Platform Admin Center, or reduce the refresh frequency temporarily.
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Use Dataflows (Optional)
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Consider offloading the load process by using Power BI Dataflows to stage your Dataverse data, then connect your semantic model to that instead.
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Thank you all for your responses. The issue hasn't been resolved, but for now, I'm applying an incremental load that creates daily records in the table and updates those that have changed within the past year. However, if I want to modify any part of the design in Power BI Desktop, I will do so using only the initial data and will need to increase the duration of the initial load.
To implement the incremental load, I used the following tutorials:
- Tutorial 1: https://www.youtube.com/watch?v=yvUNDJo6uIs&t=1015s
- Tutorial 2: https://www.youtube.com/watch?v=0vbxtT4D--Y&t=28s
- Microsoft Learn resources: Actualización incremental de los modelos semánticos en Power BI - Power BI | Microsoft Learn
Hi shimgarah
You're facing a common yet frustrating issue that can arise when working with large or complex datasets from Dataverse in Import mode within Power BI. The error message — “Microsoft SQL: The timeout period elapsed prior to completion of the operation or the server is not responding” — typically points to a backend bottleneck, likely due to query folding breakdown, inefficient API calls, or Dataverse throttling. While your optimizations like reducing row counts and removing columns are valid steps, they might not fully address the root cause if the problem lies deeper in how Power Query communicates with Dataverse.
Dataverse uses OData feeds behind the scenes, and these can be particularly sensitive to filters, data volume, and server performance. If even one query fails to fold properly (i.e., it cannot be translated into a server-side query and instead retrieves all rows before applying transformations), performance can degrade dramatically. Use the "View Native Query" option in Power Query to check whether folding is occurring for each table, and ensure any filters or transformations are placed early and preserved before folding breaks. If folding is lost, try rewriting steps or splitting them into intermediate queries.
Another area to examine is Dataverse API limitations and throttling, especially if the data is large or if multiple refreshes are happening concurrently across environments. In such cases, breaking the refresh into smaller queries using parameters or even splitting the semantic model into smaller models can help.
If this issue persists in Power BI Service, consider using a dataflow as an intermediary layer, allowing the heavy data retrieval to be done separately from the report model. Also, make sure the gateway (if you're using one) and Power BI Service are using the latest versions, and check with your Dynamics/Dataverse admin for any recent changes in performance or throttling policies.
Ultimately, the combination of native query folding, data volume control, and service layer diagnostics (like Fiddler or Performance Analyzer in Power BI Desktop) can help isolate the exact step where performance drops or failures occur. If needed, raising a Microsoft support ticket with trace logs could also reveal underlying throttling or performance caps that aren’t visible through Power BI alone.