🟧 Databricks Workspace: Databricks Workspace is a collaborative, cloud-based environment that allows you to create and work on notebooks using various programming languages such as Python, SQL, Scala, or R. You can also add libraries, new folders, and run experiments using MLflow. The Workspace provides a convenient and user-friendly interface for data analysis, machine learning, and other computational tasks.

🟧 Users section in Workspace: The Users section in the Workspace allows you to manage user access and permissions. You can add, remove, and manage users, and define their roles and access levels to different resources such as notebooks, clusters, and folders. This feature is useful when working on projects with multiple team members or when you need to restrict access to certain resources.

🟧 Reference: https://learn.microsoft.com/en-us/azure/databricks/workspace/

🟧 Databricks Repos: Databricks Repos is a version control system that is integrated with Databricks. It allows you to integrate your Databricks projects with Git repositories, making it easier to manage and collaborate on code. Repos supports all the general Git operations such as branching, merging, committing, and pulling requests. It also provides features such as code reviews, branch protection, and integrations with popular Git hosting services.

🟧 Reference: https://learn.microsoft.com/en-us/azure/databricks/repos/

🟧 Choosing between Workspace and Repos: You should choose Databricks Repos when your work involves development through Git, as it provides seamless integration with Git and allows for easy collaboration with other developers. On the other hand, if your work does not involve Git integration and you just need a collaborative environment for data analysis and machine learning, then you can use the Workspace resources alone.

In summary, Databricks Workspace is a collaborative environment for data analysis and machine learning tasks, while Repos is a version control system that is integrated with Databricks and provides Git integration for software development. Both components have their unique features and should be used based on the specific needs of your project