Which approach can satisfy these objectives?
Use Amazon Simple Storage Service (S3) with server-side encryption, and run simulations on subsets in ephemeral drives on Amazon EC2.
Use Amazon S3 with server-side encryption, and run simulations on subsets in-memory on Amazon EC2.
Use HDFS on Amazon EMR, and run simulations on subsets in ephemeral drives on Amazon EC2.
Use HDFS on Amazon Elastic MapReduce (EMR), and run simulations on subsets in-memory on Amazon Elastic Compute Cloud (EC2).
Store the full data set in encrypted Amazon Elastic Block Store (EBS) volumes, and regularly capture snapshots that can be cloned to EC2 workstations.
Explanations:
Amazon S3 with server-side encryption provides cost-effective, scalable storage for long-term needs. Using ephemeral drives for simulations ensures that temporary data does not consume persistent storage resources, aligning with cost and performance goals.
While Amazon S3 with server-side encryption is suitable, running simulations in-memory may lead to high memory consumption and cost, especially with large subsets of data. It may not efficiently manage resources for substantial simulations.
HDFS on Amazon EMR could be suitable for data processing, but using ephemeral drives for simulations does not provide the necessary long-term storage capability for large datasets and may incur additional costs.
Using HDFS on EMR for processing is appropriate, but running simulations in-memory can be expensive and impractical for large datasets, leading to inefficiencies and higher costs.
Storing the full dataset in EBS volumes can be costly, and while snapshots can be useful, this approach does not efficiently address the need for concurrent access to subsets of data or cost-effectiveness for long-term storage.
I surmise that the answer is:
Use Amazon Simple Storage Service (S3) with server-side encryption, and run simulations on subsets in ephemeral drives on Amazon EC2.