What is a key feature of Databricks that supports collaborative data science?

A key feature of Databricks that supports collaborative data science is version control for notebooks. This feature allows multiple users to work on the same notebook simultaneously while maintaining a history of changes. It provides the ability to track edits, revert to previous versions, and manage contributions from different team members, facilitating collaboration in data-driven projects.

Version control is crucial in collaborative projects as it helps prevent conflicts when multiple users are working on the same content. It also enhances the overall workflow by enabling teams to share insights, experiments, and results seamlessly. This functionality makes it easier to maintain a structured approach as data scientists iterate on their analyses and share findings with their peers.

The other options, while beneficial for different reasons, do not specifically enhance collaboration in the same impactful way. Backup and recovery options focus on data security rather than teamwork. Custom coding environments allow for individual preferences but do not facilitate group work directly, and automatic code deployment pertains more to operationalizing code rather than fostering collaboration during the development phase.