Which solution meets these requirements MOST cost-effectively?
Provision an Amazon EMR cluster Offload the complex data processing tasks.
Deploy an AWS Lambda function to add capacity to the Amazon Redshift cluster by using a classic resize operation when the cluster’s CPU metrics in Amazon CloudWatch reach 80%.
Deploy an AWS Lambda function to add capacity to the Amazon Redshift cluster by using an elastic resize operation when the cluster’s CPU metrics in Amazon CloudWatch reach 80%.
Turn on the Concurrency Scaling feature for the Amazon Redshift cluster.
Explanations:
Provisioning an Amazon EMR cluster would incur additional costs and management overhead. While it can handle complex data processing tasks, it doesn’t directly address the immediate capacity needs of the Redshift cluster during bursts.
Using a classic resize operation is not optimal for immediate bursts in usage as it can take a significant amount of time (up to hours) to complete, which won’t provide timely relief for the increased CPU demands.
Although an elastic resize operation is faster than classic resizing, it still involves a resizing process that may not provide immediate capacity for burst workloads, making it less effective for real-time demand spikes.
The Concurrency Scaling feature allows Redshift to automatically add transient capacity to handle peaks in query load without incurring the long-term costs of resizing. This solution is cost-effective and meets the requirement for consistent service during bursts of usage.
I deduce that the answer is:
Turn on the Concurrency Scaling feature for the Amazon Redshift cluster.