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In the context of designing a production streaming system on Azure Databricks, you are tasked with ensuring the system adheres to strict latency SLAs while also being cost-effective. The system must dynamically adjust to varying workloads without manual intervention. Considering these requirements, which of the following strategies would BEST meet both the performance and cost-efficiency goals? (Choose one option)
In the context of designing a production streaming system on Azure Databricks, you are tasked with ensuring the system adheres to strict latency SLAs while also being cost-effective. The system must dynamically adjust to varying workloads without manual intervention. Considering these requirements, which of the following strategies would BEST meet both the performance and cost-efficiency goals? (Choose one option)
Explanation:
The optimal solution is to leverage Azure Databricks' autoscaling feature, which dynamically adjusts resources in response to workload changes. This approach ensures that latency SLAs are met during peak times by scaling up resources and reduces costs during off-peak times by scaling down. Real-time monitoring is essential to validate that performance standards are consistently met. Options A and B represent extremes that either compromise on performance or cost, while option D is inefficient and not scalable for dynamic workloads.