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Answer: Incremental processing is a technique where only the new or changed data since the last processing run is processed, significantly reducing the amount of data handled and thus lowering costs and improving performance., Incremental processing can be implemented by tracking changes in the data source, such as using timestamps or change data capture (CDC), to identify and process only the delta, optimizing both cost and performance.
Incremental processing optimizes data pipeline performance and cost by focusing on new or changed data only, avoiding the need to reprocess the entire dataset. This approach is particularly effective in scenarios where data changes are incremental. Implementing it with mechanisms like timestamps or CDC ensures that the pipeline is both cost-efficient and scalable, as it minimizes the volume of data processed in each run and reduces latency by focusing on relevant data changes.
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A data engineering team is tasked with optimizing both the cost and performance of their data pipeline in a cloud environment. They are considering implementing incremental processing to achieve these goals. The team is particularly concerned with minimizing costs associated with data processing while ensuring that the pipeline can scale to handle increasing volumes of data without significant latency. Given this scenario, which of the following best describes incremental processing and its implementation to meet the team's objectives? (Choose two options that best apply.)
A
Incremental processing involves processing the entire dataset from scratch in each run to ensure data accuracy, which may increase costs but guarantees up-to-date information.
B
Incremental processing is a technique where only the new or changed data since the last processing run is processed, significantly reducing the amount of data handled and thus lowering costs and improving performance.
C
Incremental processing requires the use of real-time data streaming technologies to process data as it arrives, which may not always be cost-effective for all types of data pipelines.
D
Incremental processing can be implemented by tracking changes in the data source, such as using timestamps or change data capture (CDC), to identify and process only the delta, optimizing both cost and performance.