A retail company stores 100 GB of daily transactional data in Amazon S3 at periodic intervals. The company wants to identify the schema of the transactional data. The company also wants to perform…

Amazon Athena is a query service for data in S3 and while it can infer schema on read, it does not create persistent schema definitions in a catalog like Glue Crawlers do, making it less suitable for systematic schema identification and management.

AWS Glue crawlers automatically scan data in Amazon S3, infer schemas, and populate the AWS Glue Data Catalog, providing a serverless and low-overhead way to identify the schema of transactional data.

Using Amazon Redshift to store procedures for data transformations introduces the operational overhead of managing a Redshift cluster, which is not ideal for transactional data transformation directly from S3, especially when serverless options like AWS Glue are available.

AWS Glue workflows and AWS Glue jobs (using Spark or Python shell) are fully managed, serverless ETL services that can efficiently perform data transformations on data stored in Amazon S3 with minimal operational overhead.

Amazon Redshift ML allows training ML models directly within Redshift, but it still requires managing a Redshift cluster and developing custom ML models, which increases operational overhead compared to a specialized managed service like Amazon Fraud Detector for fraud detection.

Amazon Fraud Detector is a fully managed service specifically designed for detecting online fraud, using machine learning and optimized for fraud use cases, significantly reducing the operational overhead of building, training, and deploying a custom ML fraud detection model.