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A data engineer needs to optimize the performance of a data pipeline that handles retail orders. Data about the orders is ingested daily into an Amazon S3 bucket. The data engineer runs queries once each week to extract metrics from the orders data based the order date for multiple date ranges. The data engineer needs an optimization solution that ensures the query performance will not degrade when the volume of data increases. Which solution will meet this requirement MOST cost-effectively?
Correct Answer: A
Partitioning the S3 data by the order date lets Athena prune scans to only the relevant date folders, keeping query times stable as data grows. Because Athena is a serverless, pay-per- query service, you only pay for the data scanned, making it the most cost-effective way to run your weekly date-range metrics.