Explanation:
In production environments, cost efficiency is best achieved by aligning resource usage with actual data throughput. Using autoscaling clusters allows the infrastructure to scale down during low-traffic periods, while optimizing micro-batch trigger intervals helps manage the processing frequency, which directly impacts compute costs and helps meet specific performance and cost-related SLAs.
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When deploying Structured Streaming workloads in a production environment, which approach is most effective for meeting cost-related Service Level Agreements (SLAs) while maintaining performance?
A
Provisioning fixed-size clusters to ensure total cost predictability regardless of data volume.
B
Utilizing autoscaling clusters and optimizing micro-batch trigger intervals to balance resource consumption and latency.
C
Restricting job triggers to occur only during peak data arrival times to maximize compute utilization.
D
Increasing watermark thresholds significantly to ensure late-arriving data is captured in stateful operations.
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