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:
Amazon EMR is typically used for batch processing and is not a direct solution for scaling Redshift to handle spikes in complex queries. It would add extra cost and complexity without directly addressing Redshift capacity.
A classic resize operation is time-consuming and would cause downtime, which does not meet the requirement of handling bursts while maintaining availability for read and write operations.
Elastic resize in Redshift can be quicker than classic resize but is still not instantaneous and may impact availability temporarily, failing to meet the requirement of uninterrupted read and write access.
Concurrency Scaling is designed to handle unexpected bursts by automatically adding transient capacity when needed, ensuring high availability for read and write operations with cost-effective pricing for sporadic spikes.
I chart that the answer is:
Turn on the Concurrency Scaling feature for the Amazon Redshift cluster.