Which solution would meet these requirements with the LEAST expense and down time?
Use AWS Snowmobile to migrate the existing cluster data to Amazon S3. Create a persistent Amazon EMR cluster initially sized to handle the interactive workload based on historical data from the on-premises cluster. Store the data on EMRFS. Minimize costs using Reserved Instances for master and core nodes and Spot Instances for task nodes, and auto scale task nodes based on Amazon CloudWatch metrics. Create job-specific, optimized clusters for batch workloads that are similarly optimized.
Use AWS Snowmobile to migrate the existing cluster data to Amazon S3. Create a persistent Amazon EMR cluster of a similar size and configuration to the current cluster. Store the data on EMRFS. Minimize costs by using Reserved Instances. As the workload grows each quarter, purchase additional Reserved Instances and add to the cluster.
Use AWS Snowball to migrate the existing cluster data to Amazon S3. Create a persistent Amazon EMR cluster initially sized to handle the interactive workloads based on historical data from the on-premises cluster. Store the data on EMRFS. Minimize costs using Reserved Instances for master and core nodes and Spot Instances for task nodes, and auto scale task nodes based on Amazon CloudWatch metrics. Create job-specific, optimized clusters for batch workloads that are similarly optimized.
Use AWS Direct Connect to migrate the existing cluster data to Amazon S3. Create a persistent Amazon EMR cluster initially sized to handle the interactive workload based on historical data from the on-premises cluster. Store the data on EMRFS. Minimize costs using Reserved Instances for master and core nodes and Spot Instances for task nodes, and auto scale task nodes based on Amazon CloudWatch metrics. Create job-specific, optimized clusters for batch workloads that are similarly optimized.
Explanations:
This option utilizes AWS Snowmobile for data migration, which is suitable for large datasets like 20 PB. It proposes creating a persistent EMR cluster optimized for the existing workloads while using Reserved Instances and Spot Instances to minimize costs. Additionally, it includes auto-scaling based on CloudWatch metrics, ensuring efficient resource utilization and resiliency. Job-specific optimized clusters for batch workloads further enhance performance and cost-effectiveness.
Although this option also uses AWS Snowmobile for data migration, it suggests creating a persistent EMR cluster of similar size to the current cluster without the optimization strategies mentioned in Option A. It does not include auto-scaling or Spot Instances, which would limit cost efficiency and flexibility. Purchasing additional Reserved Instances as the workload grows may lead to higher costs and does not address the need for resiliency effectively.
This option proposes using AWS Snowball for data migration, which is less suitable for transferring 20 PB of data compared to Snowmobile. While it includes a similar configuration for the EMR cluster and cost optimization strategies like Reserved Instances and Spot Instances, the choice of Snowball limits the effectiveness of the migration process and may incur longer downtime. Additionally, it lacks the emphasis on optimizing job-specific clusters for batch workloads.
This option suggests using AWS Direct Connect for data migration, which is primarily designed for establishing a dedicated network connection rather than transferring large datasets like 20 PB efficiently. It does include some good strategies for cluster configuration and cost management, but the migration method would likely lead to significant downtime and increased costs compared to using Snowmobile. Therefore, it does not meet the requirements as effectively as Option A.
I design that the answer is:
Use AWS Snowmobile to migrate the existing cluster data to Amazon S3. Create a persistent Amazon EMR cluster initially sized to handle the interactive workload based on historical data from the on-premises cluster. Store the data on EMRFS. Minimize costs using Reserved Instances for master and core nodes and Spot Instances for task nodes, and auto scale task nodes based on Amazon CloudWatch metrics. Create job-specific, optimized clusters for batch workloads that are similarly optimized.