Which type of table is more flexible and ideal for large datasets?

Unmanaged tables are indeed considered more flexible and ideal for handling large datasets in data platforms like Databricks. This type of table allows users the freedom to manage the underlying data files stored in external locations (like cloud storage) independently of the table itself. When a user creates an unmanaged table, they do not have to worry about the lifecycle management of the data, as it remains in its original location, allowing for easier access and manipulation.

One of the advantages of unmanaged tables is that they do not consume storage space within the data warehouse, which helps in optimizing performance and cost, especially when dealing with extensive datasets. Furthermore, because they are not tied to the database's storage management, users can efficiently handle updates or delete operations directly on the data stored externally.

In contrast, managed tables are fully controlled by the database, which means the storage and data lifecycle are tightly integrated. This can lead to challenges in terms of flexibility, especially as the dataset grows. Temporary tables are designed for short-lived use cases and may not be suitable for extensive data operations. Cluster-scoped tables, while useful in specific contexts, are limited to the lifetime of the cluster and are less appropriate for managing large and persistent datasets.

Thus, the flexibility of unmanaged tables in managing