Outage in Codat

QuickBooks Desktop: Some larger datasets are failing with Fetch Errors

Resolved Minor
August 22, 2025 - Started 24 days ago - Lasted 13 days
Official incident page

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Outage Details

We are actively investigating an issue which is intermittently affecting datasets from QuickBooks Desktop companies. Affected users will notice that some datasets fail with a FetchError. This is usually when the dataset is slightly larger in size than average.
Components affected
Codat QuickBooks Desktop
Latest Updates ( sorted recent to last )
RESOLVED 11 days ago - at 09/04/2025 03:22AM

The incident has now been resolved, ongoing monitoring has shown the Quickbooks Desktop platform has returned to normal stability.

All users previously affected should be able to pull data without disruption.

If you are still affected by this issue, please contact support@codat.io

MONITORING 19 days ago - at 08/27/2025 06:16PM

We have implemented a fix. Affected users should notice that Quickbooks Desktop syncs have returned to normal.

Our teams continue to monitor the platform to ensure continued stability. If you experience any issues, please contact support@codat.io for further assistance.

INVESTIGATING 19 days ago - at 08/27/2025 03:37PM

We are still actively investigating this issue. Affected clients will notice that their QuickBooks Desktop connections are unable to complete a read sync (pull operation) in Production for some datasets. Datasets affected will typically return a status of 'fetchError'.

While we are still looking into this issue, as a partial workaround, we recommend clients to reduce the range of data synced for affected connections.

Please contact support@codat.io for further assistance on reducing the range of the data synced for your affected connections.

INVESTIGATING 21 days ago - at 08/25/2025 06:51PM

We are actively investigating an issue which is intermittently affecting datasets from QuickBooks Desktop companies.

Affected users will notice that some datasets fail with a FetchError. This is usually when the dataset is slightly larger in size than average.

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