Delta Table

A Delta Table is a regular table in Databricks that uses the Delta Lake format. Not to be confused with Delta Live Tables.
There are two types of delta tables managed and unmanaged (external).

# Managed Delta Tables

• Definition: In a managed table, Databricks takes full control of both the data storage and metadata associated with the table.
• Storage: The data is stored in the Databricks File System (DBFS) or a location that Databricks manages internally (like an S3 bucket or Azure Blob Storage under Databricks’ control).
• Lifecycle Management: When you create a managed Delta Table, you don’t have to worry about specifying the file path. If you drop or delete the table, the underlying data files are also deleted automatically by Databricks.
• Use Case: Managed tables are typically used when you want Databricks to handle all aspects of table management, including storage, metadata, and cleanup.

# Unmanaged (External) Delta Tables

• Definition: An unmanaged (or external) table is where you provide a specific file path for the table’s data. While Databricks manages the metadata, the underlying data storage is managed separately by the user.
• Storage: The data files are stored in a specified location (like your S3 bucket, Azure Data Lake, or any other file system). Databricks only keeps track of the metadata for the table, not the actual files.
• Lifecycle Management: If you drop the table, Databricks will remove the metadata but will not delete the underlying data files. You must manually manage data cleanup.
• Use Case: Unmanaged tables are used when you want more control over where the data is stored, or if you want to share the data files across different systems.