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Answer: Increase the frequency of checkpoints, set aggressive watermarking thresholds, and implement robust exception handling and interruption recovery mechanisms.
Option D is the most comprehensive approach. Increasing the frequency of checkpoints ensures that the system frequently saves its state, reducing the risk of data loss during interruptions. Aggressive watermarking thresholds help in processing late-arriving data more efficiently, which is crucial for analytical purposes. Robust exception handling and interruption recovery mechanisms ensure that the pipeline can quickly recover from any disruptions, maintaining the flow of data to the analytics team.
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Consider a scenario where you are tasked with optimizing a stream processing pipeline for analytical purposes. The pipeline currently processes real-time data from IoT devices, but the analytics team is experiencing delays in receiving processed data. Describe the steps you would take to optimize this pipeline, including how you would configure checkpoints and watermarking, and how you would handle interruptions and exceptions.
A
Increase the frequency of checkpoints and reduce watermarking thresholds to minimize data delay.
B
Decrease the frequency of checkpoints and increase watermarking thresholds to improve processing speed.
C
Maintain the current checkpoint and watermarking settings but implement a fault-tolerant mechanism to handle interruptions.
D
Increase the frequency of checkpoints, set aggressive watermarking thresholds, and implement robust exception handling and interruption recovery mechanisms.