In Databricks, how is version control typically implemented?

Version control in Databricks is typically implemented through Git integration for code management. This integration allows users to connect their Databricks environment directly to external Git repositories, facilitating collaborative development and version management of notebooks and other code artifacts.

Using Git, developers can track changes made to their code, revert to previous versions, and manage branches for different features or experiments. This not only enhances collaboration among team members but also provides a robust mechanism for ensuring that the latest code changes are easily accessible and manageable.

The use of local file backups, built-in Databricks versioning tools, or access restrictions does not offer the same level of functionality or flexibility in version control as Git integration. Local backups may safeguard against data loss but do not provide version tracking capabilities. Built-in versioning tools in Databricks do provide some form of version management, but they typically do not match the comprehensive and widely adopted functionality of Git, which is standard in software development. Restricting access may help in managing permissions, but it does not inherently provide a version control mechanism. Implementing Git integration allows for a systematic approach to managing code changes and collaboration.