Hi @Suheb,

That’s a great question, version control is one of the most important things to get right early on.

As a best practice, you should never run notebooks directly in production. Instead, notebooks should be treated as development assets, once validated, they should be packaged, version-controlled, and deployed through proper CI/CD.

1. Use Git integration

• Databricks integrates directly with GitHub, Azure DevOps and GitLab.

• Always link your workspace to a Git repo and commit your notebook changes regularly, this keeps full version history and supports collaboration.

2. Package and deploy, don’t run manually

• Convert notebooks into production-ready code (Python modules or .py scripts).

• Use Databricks Asset Bundles (DAB) or your CI/CD pipeline to deploy jobs, pipelines, and workflows, not raw notebooks.

• This ensures environments (Dev, Test, Prod) stay consistent and auditable.

3. Automate with Workflows

• Use Jobs or Workflows to orchestrate your pipelines instead of manual runs.

• Parameters, retries, and alerts can all be managed centrally.

4. Keep documentation handy

• Databricks provides extensive documentation for both Git integration and CI/CD with DAB, plenty of examples depending on your setup (GitHub Actions, Azure DevOps, Jenkins, etc.).

In short:

Develop in notebooks, version in Git, deploy with DAB, and run in production via Jobs/Workflows never directly from a notebook.