Hey @Hari_P ,

I believe this doesn’t exist today as a built-in feature. I reviewed the Databricks notebook permission model (docs link) and with the minimum level (“CAN READ”) users already have access to view the notebook’s source.

The simplest and most robust approach seems to be sticking with the wheel package. You can also use wheels for colleagues within the same workspace. Depending on your use case, you might want to check the installation options here: Python library support.

I tested a wrapper approach (a child notebook calling a parent notebook while restricting access to the parent), but it doesn’t work that way. So, in my opinion, the most sensible and convenient solution is to package your code into a wheel. I’d also recommend setting up some CI/CD pipeline to recompile the wheel automatically and publish the new version, so that everyone can update their clusters with the latest code without manual steps.

Hope this helps, 🙂

Isi