What should the solutions architect recommend to build the MOST high-performing solution?
Use AWS Glue to process data and Amazon S3 to store data.
Use Amazon EMR to process data and Amazon Redshift to store data.
Use Amazon EC2 to process data and Amazon Elastic Block Store (Amazon EBS) to store data.
Use Amazon Kinesis Data Analytics to process data and Amazon Elastic File System (Amazon EFS) to store data.
Explanations:
AWS Glue is an ETL service, but it is not designed for high-performance big data processing at scale. Storing data in Amazon S3 is cost-effective and scalable, but for high-performance SQL query access, it may not be optimal without additional query engines like Amazon Athena.
Amazon EMR is optimized for big data processing (Hadoop, Spark, etc.) and Amazon Redshift is a fully managed data warehouse that supports SQL queries and BI tools. This combination provides high performance for both processing and querying large datasets.
Amazon EC2 can process data, but it is not specifically optimized for big data processing at scale. Amazon EBS is more suitable for individual instances and does not scale efficiently for large data processing like S3 or Redshift.
Amazon Kinesis Data Analytics is designed for real-time streaming data processing, not batch processing of large structured or semi-structured data. Amazon EFS is more appropriate for file storage but does not provide the same performance or cost-effectiveness as S3 for large-scale data.
If I’m correct, the answer is:
Use Amazon EMR to process data and Amazon Redshift to store data.