Which description best fits Delta Lake?

Delta Lake is best described as a relational storage layer for Spark based on Parquet files. This is because Delta Lake enhances the capabilities of Apache Spark by adding features such as ACID transactions, scalable metadata handling, and unifying streaming and batch data processing. It allows users to work with data stored in Parquet format while benefiting from the transactional capabilities that Delta Lake provides.

This combination of features improves the management of data lakes, allowing for more efficient query performance and data consistency even as data is being modified. By leveraging the strengths of Parquet, Delta Lake ensures that data can be stored in a highly efficient columnar format while also maintaining the integrity and reliability of operations on that data.

The other choices do not encapsulate the primary features and purpose of Delta Lake:

• The first choice refers to an API, which does not accurately represent Delta Lake's function.
• The third choice focuses on synchronization solutions, which involve different capabilities not central to Delta Lake's core functionality.
• The last option is limited to batch processing while Delta Lake supports both batch and streaming data processes, highlighting its versatility and strength in managing data workflows effectively.