A data engineer subscribed an S3 bucket to an AWS Data Exchange product that delivers economic indicators. The engineer must join that incoming economic data with an existing table in Amazon Athena and complete all transformations within 30–60 minutes. Which cost-effective solution meets these requirements?

The correct solution leverages an S3 event to trigger a Lambda function, which then initiates an AWS Glue job. This approach is cost-effective because Lambda and Glue are serverless, meaning you only pay for compute time used. AWS Glue is well-suited for ETL (Extract, Transform, Load) operations, including joining data from S3 with Athena tables, and can handle the required transformations within the 30-60 minute timeframe. Writing the result back to S3 maintains a cost-effective storage solution. Incorrect options: Kinesis Data Streams and Firehose are designed for real-time streaming, which is overkill and more expensive for a batch process triggered by new S3 objects. SageMaker Data Wrangler is primarily a data preparation tool within SageMaker, not typically used for direct integration with Athena tables via Lambda in this manner, and might be less cost-effective for simple merges. Provisioning an Amazon Redshift cluster for this task is significantly more expensive due to continuous cluster costs, even if not constantly utilized, and introduces unnecessary complexity for this specific requirement.