Explanation:
To meet cost SLAs in production streaming, it is crucial to align resource consumption with workload demand. Autoscaling clusters allow Databricks to dynamically adjust the number of workers based on the streaming backlog. Additionally, optimizing micro-batch trigger intervals (such as adjusting the trigger setting) ensures that the cluster processes data efficiently, avoiding the overhead of processing micro-batches that are too small or frequent while still meeting latency requirements.
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When deploying Structured Streaming jobs in a production environment, which approach is most effective for optimizing costs while adhering to Service Level Agreements (SLAs)?
A
Utilizing fixed-size clusters to ensure cost predictability regardless of data volume fluctuations.
B
Implementing autoscaling clusters and optimizing micro-batch trigger intervals to balance resource usage with processing demand.
C
Configuring jobs to trigger exclusively during peak data arrival times to minimize idle compute cycles.
D
Increasing watermark thresholds to ensure all late-arriving data is captured before state expiration.