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Answer: Delta Lake
Delta Lake is the correct choice because it provides ACID transactions on top of Spark's data processing capabilities. This ensures that complex operations like updates, deletes, and merges are handled in a way that maintains data integrity, even with multiple processes reading from and writing to the dataset simultaneously. Delta Lake's transaction log records every change, enabling atomic and consistent operations, isolation among concurrent transactions, and durability to prevent data loss. This makes Delta Lake ideal for managing large datasets with high reliability and consistency.
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When implementing a data pipeline that requires updates, deletions, and merges into a large dataset stored on a distributed file system, what feature should a data engineer use to ensure these operations are performed reliably and consistently, with the ability to handle concurrent modifications without data corruption or loss?
A
Spark Streaming
B
Delta Lake
C
Dataframe caching
D
File compaction
E
Data partitioning
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