Here is a basic difference between managed table and external table:

Managed Tables 

• Managed tables are fully controlled by Databricks, including both the data and metadata lifecycle.
• Data is stored in a Databricks-managed storage location configured by Unity Catalog.
• These tables always use delta table.
• Dropping a managed table also deletes its underlying data files.
• Ideal for Databricks-native workflows where Databricks handles the organization and lifecycle of data.
• Metadata is maintained in Unity Catalog and tightly integrated with Databricks-managed storage.
• Managed tables benefit from Databricks' built-in storage optimizations for better performance.
• Fully integrated with Unity Catalog’s data governance features, including access controls for both metadata and data.
• Migration involves additional effort to extract and relocate data since it is stored within Databricks' infrastructure.
• Best suited for new projects built entirely within Databricks.

External Tables 

• External tables are partially controlled by Databricks, where only the metadata is managed while the data resides in external storage.
• Data is stored in external locations such as AWS S3, Azure Blob Storage, or ADLS.
• These tables can use delta, parquet, csv, etc.
• Dropping an external table does not delete the underlying data files only the meta-data is deleted.
• Best suited for referencing existing datasets or integrating data stored in external systems without moving it into Databricks.
• Metadata is maintained in Unity Catalog, pointing to the external data location.
• Performance depends on the configuration and capabilities of the external storage system.
• Unity Catalog governs metadata, but the governance of the actual data relies on external storage’s access controls.
• Migration is straightforward as the data is already stored externally.
• Best suited for integrating external data lakes or datasets shared across multiple systems.