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Answer: Implementing autoscaling clusters and optimizing micro-batch trigger intervals to balance resource usage with processing demand.
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.
Author: LeetQuiz Editorial Team
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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.
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