A legacy application produces CSV files and stores them in Amazon S3. A new commercial off‑the‑shelf (COTS) application can run complex SQL queries only against data in Amazon Redshift and Amazon S3, but it cannot process .csv files. The legacy application cannot be changed. Which solution enables the COTS application to use the legacy data with the LEAST operational overhead?
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Correct answer: Create an AWS Glue extract, transform, and load (ETL) job that runs on a schedule. Configure the ETL job to process the .csv files and store the processed data in Amazon Redshift..
Why this is the answer
AWS Glue is a fully managed ETL service that can process CSV files and load them into Amazon Redshift, directly addressing the requirements. Scheduling the Glue job provides automation with minimal operational overhead. Developing a Python script on EC2 introduces server management overhead and doesn't directly load into Redshift. Using Lambda and DynamoDB is incorrect because the COTS application requires data in Redshift or S3, not DynamoDB. While EMR can perform ETL, it generally has higher operational overhead than AWS Glue for scheduled, serverless ETL tasks.
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