What feature is known for automatically compacting Delta tables in Fabric?

The feature that is known for automatically compacting Delta tables in Fabric is indeed the lakehouse. The lakehouse architecture integrates data lake and data warehouse functionalities, allowing for efficient data storage and management. In this context, one of the key advantages of using a lakehouse is its ability to optimize data storage, which includes automatically compacting Delta tables. This automatic compaction helps improve performance by reducing the number of small files that can slow down query execution and overall data processing.

The lakehouse approach leverages the strengths of both data lakes (for unstructured or semi-structured data) and data warehouses (for structured data) while providing advanced capabilities such as ACID transactions, schema enforcement, and the ability to run various analytics workloads seamlessly. Automatic compaction fits naturally into this model, ensuring that data remains manageable and query performance stays optimized.

Dataflows, while a tool for data processing and transformation, do not specifically handle the compaction processes of Delta tables. Similarly, a data warehouse, though integral for structured data analysis, does not have the same focus on the nuances of Delta table management as the lakehouse architecture does. An Azure SQL database offers robust database functionalities but is not aligned with the Delta table compaction feature specific to the lakehouse paradigm.