Which type of table is designed to be managed by the Databricks platform?

The type of table designed to be managed by the Databricks platform is managed tables. Managed tables are those where the Databricks system takes full responsibility for the data lifecycle, including storage and retention. When you create a managed table, Databricks stores the data in its own storage, and if you delete the table, the data associated with it is also deleted. This makes managed tables particularly useful when you want Databricks to handle the underlying data management and ensure that the data is optimized for performance and integration within the Databricks environment.

Unmanaged tables refer to tables where the data is stored outside of Databricks' control, often in external storage solutions. Although you can use these tables within Databricks, the platform does not manage the data or the lifetime of the storage linked to these tables.

External tables are similar to unmanaged tables in that they are linked to data stored outside of Databricks. They allow querying of data located in external sources, but the management of the data is outside of the Databricks framework.

Partitioned tables are simply a method of dividing a large table into smaller, more manageable pieces based on column values. They can be either managed or unmanaged, but the term 'partitioned' refers