Explanation:
Bloom filters are a highly effective indexing technique in Delta Lake for optimizing joins on columns with high cardinality. They function as a data-skipping mechanism that allows the Spark engine to quickly determine if a value might exist in a file, thereby significantly reducing unnecessary data scanning and I/O overhead during join operations.
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When working with Delta Lake, which indexing technique is most effective for optimizing join performance on columns characterized by high cardinality?
A
Z-ordering
B
Partitioning
C
Bloom filters
D
Adjusting file sizes
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