Hi @emorgoch,
Thanks for raising this. This appears to be a regression rather than expected behaviour. Internally, the issue has been identified around .ipynb handling in Git folders, and the intended fix is to stop serialising these execution timestamp fields when outputs are not being exported. So the behaviour youโre seeing is being addressed and is not the intended long-term state. I don't have an ETA for this though.
At the moment, the documented controls focus on the notebook format itself and whether notebook outputs are committed, but there isnโt a documented setting in the web UI that strips only specific cell metadata fields, such as startTime, finishTime, and submitTime, during commit.
If your main goal is to reduce review noise, the two supported options today are either to switch those notebooks to Databricks source format, which is more lightweight for version control, or to stay on .ipynb and manage whether outputs are included through the commit_outputs configuration. You can see the notebook format options here in the Manage notebook format docs, and the broader Git folders behaviour here in Databricks Git folders and Create and manage Git folders.
If you need to stay with .ipynb because you want richer notebook fidelity, dashboards, or visualisations, that format does support those features better than the source format. The tradeoff is that .ipynb is a richer representation, so it can be less clean in source control than plain source notebooks. The docs call out that source format is the simpler code-only representation, while .ipynb captures notebook structure and optional outputs.
For teams working primarily in the web IDE, there isnโt a documented pre-commit hook mechanism in the standard Git folders flow to automatically wipe just those timestamp fields before commit. If you are using a local clone or Git CLI-based workflow, then a custom pre-commit hook outside Databricks could sanitise those fields before pushing, but that would be a Git-side workaround rather than a built-in Databricks setting.
If this answer resolves your question, could you mark it as โAccept as Solutionโ? That helps other users quickly find the correct fix.
Regards,
Ashwin | Delivery Solution Architect @ Databricks
Helping you build and scale the Data Intelligence Platform.
***Opinions are my own***