A company is developing a platform to process large volumes of data for complex analytics and machine learning (ML) tasks. The platform must handle compute-intensive workloads. The workloads currently require 20 to 30 minutes for each data processing step.
The company wants a solution to accelerate data processing.
Which solution will meet these requirements with the LEAST operational overhead?
Question:
A finance company collects streaming data for a real-time search and visualization system. They want to migrate to AWS using a native solution for ingest, search, and visualization.
Options:
A company recently migrated a data warehouse to AWS. The company has an AWS Direct Connect connection to AWS. Company users query the data warehouse by using a visualization tool. The average size of the queries that the data warehouse returns is 50 MB. The average visualization that the visualization tool produces is 500 KB in size. The result sets that the data warehouse returns are not cached.
The company wants to optimize costs for data transfers between the data warehouse and the company.
Which solution will meet this requirement?
A company wants to run a hybrid workload for data processing. The data needs to be accessed by on-premises applications for local data processing using an NFS protocol, and must also be accessible from the AWS Cloud for further analytics and batch processing.
Which solution will meet these requirements?
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