Partial Sync Failure: When Only Some Records Are Wrong
Partial failures are easy to miss because most of the integration can be correct while only a small subset requires investigation.
Why partial failures stay hidden
High-level run status and total counts can look healthy even when individual records are wrong.
Compare each expected identifier
Record-level reconciliation verifies the individual source records rather than assuming the whole batch is correct.
Separate clean and issue counts
Report how many records are OK and how many fall into each exception category.
Frequently asked questions
What is a partial sync failure?
It is a sync where part of the expected dataset is correct while another subset is missing, different, duplicated, or otherwise incorrect.
Do not stop at integration success.
Compare the actual source and target data after the workflow finishes.
Start verifying→