A solutions architect needs to optimize a large data analytics job that runs on an Amazon EMR cluster. The job takes 13 hours to finish. The cluster has multiple core nodes and worker nodes deployed on large, compute-optimized instances.
After reviewing EMR logs, the solutions architect discovers that several nodes are idle for more than 5 hours while the job is running. The solutions architect needs to optimize cluster performance.
Which solution will meet this requirement MOST cost-effectively?
EMR managed scaling dynamically resizes the cluster by adding or removing nodes based on the workload. This feature helps minimize idle time and reduces costs by scaling the cluster to meet processing demands efficiently.
Option A: Increasing the number of core nodes might increase idle time further, as it does not address the root cause of underutilization.
Option C: Migrating the job to Lambda is infeasible for large analytics jobs due to resource and runtime constraints.
Option D: Changing to memory-optimized instances may not necessarily reduce idle time or optimize costs.
AWS Documentation Reference:
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