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Answer: It records row-level changes in the transaction log, enabling applications to incrementally query and process only the specific modifications.
Change Data Feed (CDF) simplifies incremental propagation by capturing row-level changes—including inserts, updates, and deletes—directly within the Delta Lake transaction log. This allows downstream processes to efficiently consume only the data that has changed, avoiding the high overhead associated with full table scans or complex manual diffing logic.
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In the context of incremental data processing, how does Delta Lake's Change Data Feed (CDF) help resolve the challenges of propagating updates and deletes to downstream consumers?
A
It automatically triggers and applies DML operations directly onto all registered downstream tables within the workspace.
B
It generates a separate physical copy of the table that exclusively stores updated and deleted records to optimize storage costs.
C
It records row-level changes in the transaction log, enabling applications to incrementally query and process only the specific modifications.
D
It mandates a full-table overwrite strategy for every commit to ensure consistency across the entire data pipeline.
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