When building a pipeline, you may need to roll back a dataset to an earlier version. There may be various reasons for this, including the following:
The dataset rollback feature allows you to update the data and job history of a dataset. If the dataset is being built incrementally, the dataset rollback feature also ensures that the incrementality of your dataset is preserved.
The two types of possible rollbacks on a dataset are as follows:
If you accidentally force a snapshot on the next build of a dataset, but you intended to roll back to an earlier transaction, do not proceed with a rollback, as this could leave the dataset in a partially rolled back state.
Instead, build the dataset; it will run as a snapshot since the dataset was configured to snapshot on the next build, and then carry out the intended rollback.
When rolling back a dataset, keep the following considerations in mind:
Editor role.



Acknowledge the warning that a rollback cannot easily be undone and select Rollback dataset.
Once the rollback is complete, navigate to the dataset's History tab and ensure that the rolled back transactions are now crossed out, as shown below:

If a dataset backs an object type stored using object storage v2, manual intervention is required to ensure that the object type is reindexed with a successful run of the replacement pipeline to reflect the state after the rollback.
Forcing a snapshot will not change the dataset’s transaction history or produce immediate visible changes. The snapshot will occur on the next build.
Forcing a snapshot will require a force build to rebuild the dataset if there are no changes to either the input data or the logic backing the dataset.



