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Machine Learning
Dive into the world of machine learning on the Databricks platform. Explore discussions on algorithms, model training, deployment, and more. Connect with ML enthusiasts and experts.
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Best Practices for Collaborative Notebook Development in Databricks

tarunnagar
New Contributor
Hi everyone! 

I’m looking to learn more about effective strategies for collaborative development in Databricks notebooks. Since notebooks are often used by multiple data scientists, analysts, and engineers, managing collaboration efficiently is critical for productivity and project quality.

Specifically, I’m curious about:
  • Version control approaches: How do you manage multiple contributors working on the same notebook?
  • Code organization: Tips for structuring notebooks for readability and reusability in team environments.
  • Collaboration features: How do you make the best use of Databricks’ built-in commenting, review, and workflow features?
  • Testing & validation: Techniques for ensuring changes don’t break downstream workflows or pipelines.
  • Team workflows: Best practices for dividing work, merging updates, and maintaining a consistent coding standard across team members.
I’d love to hear from professionals who have experience with collaborative projects in Databricks — what works well, what pitfalls to avoid, and any practical tips or tools that improve team productivity.

Looking forward to your insights and experiences! 
1 REPLY 1

AbhaySingh
New Contributor

For version control, use this approach.

Git Integration with Databricks Repos


Core Features:

Databricks Git Folders (Repos) provides native Git integration with visual UI and REST API access 
Supports all major providers: GitHub, GitLab, Azure DevOps, Bitbucket (cloud and on-premises) 
Authentication via OAuth (recommended), Personal Access Tokens, or service principal credentials 
Built-in conflict resolution tools with visual diff comparison and three resolution methods: manual editing, keep current, or take incoming changes  
Sparse checkout support for large repositories using cone patterns (clone only specific directories) 
Two folder types: user-level Git folders for development (/Workspace/Users/<email>/) and production Git folders (outside user folders with restricted access)