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Answer: Create a hierarchical folder structure within the workspace, segregating notebooks into separate folders based on their task (e.g., ingestion, processing, ML training) and further organizing them by functionality or team.
Option B is the most effective for organizing notebooks in a Databricks workspace to enhance maintainability, scalability, and collaboration. A hierarchical folder structure allows for logical segregation of notebooks by task and functionality, making it easier for team members to locate and work with the notebooks. This approach supports scalability as the number of notebooks grows. Option A, while simple, becomes inefficient and hard to navigate as the number of notebooks increases. Option C complicates collaboration by dispersing notebooks across workspaces. Option D introduces unnecessary complexity and may hinder direct access and version control of notebooks.
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In a Databricks workspace, your team is working on a project that involves multiple notebooks performing various tasks including data ingestion, data processing, and machine learning model training. The project is expected to scale over time, adding more notebooks and team members. Considering the need for maintainability, scalability, and collaboration, what is the BEST way to organize these notebooks? (Choose one option.)
A
Maintain all notebooks in a single folder with descriptive naming conventions to differentiate tasks, despite the potential increase in volume.
B
Create a hierarchical folder structure within the workspace, segregating notebooks into separate folders based on their task (e.g., ingestion, processing, ML training) and further organizing them by functionality or team.
C
Distribute notebooks across multiple Databricks workspaces according to their task, utilizing Databricks Repos for version control and access management.
D
Develop a custom web application to host all notebooks, using the application's navigation to switch between tasks, thereby centralizing access.