A rapidly expanding research division is rolling out a deep analysis platform that processes large data sets at unpredictable intervals. The application requires a high number of input/output operations and data encryption. It must also stay operational during spikes in demand and confirm that data is retained for compliance requirements. Which option meets these criteria?
HPC nodes with ephemeral storage that do not keep data beyond processing cycles
Block-based storage with encryption and compute nodes configured for high IOPS
Object-based resources with encryption that emphasize long-term archival over responsiveness
Local drives with limited encryption and minimal data retention capability
A block-based resource with encryption, provisioned for high IOPS, and paired with load-balanced compute nodes can handle unpredictable bursts, ensure sensitive data is secure, and maintain the necessary throughput. An ephemeral-only approach is not designed for compliance-focused retention. A platform focused on object-based storage does not emphasize high IOPS, which is important for compute-intensive tasks. A dedicated HPC (High-Performance Computing) environment with ephemeral storage meets certain performance criteria but does not align well with regulatory data retention needs.
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What is IOPS, and why is it important for deep analysis platforms?
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What makes block-based storage suitable for compliance-focused data retention?
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How do load-balanced compute nodes help with handling unpredictable bursts in demand?