> For the complete documentation index, see [llms.txt](https://cleyrop.gitbook.io/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://cleyrop.gitbook.io/docs/documentation-fr-en/data-and-ai-project/dataflow/creer-un-dataflow/nettoyer-ou-supprimer-un-dataflow.md).

# Clean up or delete a Dataflow

Cleyrop allows you to delete a Dataflow that has become obsolete while retaining the execution traces necessary for auditing.

Before any deletion, it is recommended to check dependencies (produced datasets, linked Dataflows, active schedules).

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## Delete a Dataflow

From the Dataflow list, select the <i class="fa-pen">:pen:</i> `Information` or from the Dataflow click on <i class="fa-gear">:gear:</i> `Information`, then `Delete`. Confirm the deletion when the validation window appears.

#### Effects of deletion

* The Dataflow (and its transformation graph) is deleted from the project space.
* The produced datasets are not deleted automatically. If they were referenced in the project library, they become orphaned but remain available in the dataset library.
* You can view, delete, or reuse them like any other dataset.
* The execution history and logs remain available for audit and traceability purposes.

{% hint style="success" %}
This ensures that no data is lost and that you can trace the processing performed, even after a Dataflow has been deleted.
{% endhint %}

### Clean up orphaned datasets

After deleting a Dataflow, it is advisable to check the produced datasets that are no longer linked to any active flow.

1. Go to the dataset library.
2. Use the Origin filter: *Dataflow*
3. Delete or archive datasets that are no longer used. Those from a deleted Dataflow will be labeled "Deleted"

### Recommendations before deletion

* Make sure the Dataflow is not referenced in other dependent Dataflows.
* Disable automatic schedules before deletion.
* Notify the users concerned to avoid accidentally deleting a flow that is still in use.

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## Best practices

* Regularly delete test or obsolete Dataflows to keep the space tidy.
* Clean up orphaned datasets at least once a quarter.