Manually review each notebook and dataset periodically to identify and archive those that are no longer in use, ensuring direct control over what is archived.

Utilize the Databricks API to develop an automated workflow that identifies and archives notebooks and data based on customizable criteria such as last accessed date or size, ensuring consistency and reducing manual effort.

Rely on Databricks' built-in data retention policies to automatically manage the archiving and deletion of old notebooks and data, leveraging out-of-the-box functionality without additional development.

Develop a custom script that periodically scans the workspace for old notebooks and data, then moves them to a separate storage location, offering flexibility but requiring ongoing maintenance.