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Helper IThe XML for Analysis request timed out before it was completed. Timeout value: power bi
Hi amal_01 ,
This is a pretty common pain point when refreshing Power BI datasets from D365 OData feeds.
The “XML for Analysis request timed out” error with a value like 7200 seconds is coming from the D365 OData service, not from Power BI itself. Power BI Service actually allows a longer timeout for refreshes (usually 2–5 hours), but if D365 cuts off the query after 7200 seconds (2 hours), Power BI can’t do anything about it. Changing the timeout in Power Query or M won’t help here, because the data source’s own limit always wins.
Try to filter your queries as much as possible, or break them into smaller parts so each one finishes faster. If you’re using Premium, consider setting up Incremental Refresh, this can really help with large datasets. You can ask your D365 admin if the OData timeout can be raised, but that’s rarely done in production. Some teams use Azure Data Factory or Power BI Dataflows to stage the data elsewhere first, especially if dealing with massive volumes.
The root cause is the timeout on the D365 side. Focusing on query optimization or incremental loading is usually the best way forward.
Super UserHi amal_01 ,
This is a pretty common pain point when refreshing Power BI datasets from D365 OData feeds.
The “XML for Analysis request timed out” error with a value like 7200 seconds is coming from the D365 OData service, not from Power BI itself. Power BI Service actually allows a longer timeout for refreshes (usually 2–5 hours), but if D365 cuts off the query after 7200 seconds (2 hours), Power BI can’t do anything about it. Changing the timeout in Power Query or M won’t help here, because the data source’s own limit always wins.
Try to filter your queries as much as possible, or break them into smaller parts so each one finishes faster. If you’re using Premium, consider setting up Incremental Refresh, this can really help with large datasets. You can ask your D365 admin if the OData timeout can be raised, but that’s rarely done in production. Some teams use Azure Data Factory or Power BI Dataflows to stage the data elsewhere first, especially if dealing with massive volumes.
The root cause is the timeout on the D365 side. Focusing on query optimization or incremental loading is usually the best way forward.