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Answer: Incremental processing involves processing only new or changed data since the last run, which is more complex but can be more efficient than batch processing.
Incremental processing is a method where only new or changed data since the last run is processed, which can be more efficient than batch processing that processes all data regardless of changes. Key considerations for implementing incremental processing effectively include identifying changes in data, managing dependencies, and ensuring data consistency across runs.
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Explain the process of incremental processing in a data pipeline. How does it differ from batch processing, and what are the key considerations for implementing incremental processing effectively?
A
Incremental processing involves processing only new or changed data since the last run, which is more complex but can be more efficient than batch processing.
B
Incremental processing is identical to batch processing but runs more frequently, which can lead to increased resource usage.
C
Incremental processing requires no special considerations as it automatically detects changes in the data, making it simpler to implement than batch processing.
D
Incremental processing should be avoided in favor of batch processing due to its complexity and potential for data inconsistencies.