A partner sends plaintext credit-card data (about 5,000 records every 15 minutes) into an S3 bucket (server-side encrypted). The company must automatically mask PANs, remove/merge fields, and transform records into JSON, then send results to another S3 bucket for internal processing. The design should be easily extensible for additional feeds. Which solution satisfies these requirements?

AWS Glue is ideal for this scenario because it provides a serverless, scalable ETL (Extract, Transform, Load) service. The Glue crawler with a custom classifier automatically infers the schema of the incoming plaintext data, even with its unique format, and creates a table definition in the Glue Data Catalog. A Lambda function can then trigger a Glue ETL job, which uses Apache Spark to efficiently perform the required transformations: masking PANs, removing/merging fields, and converting to JSON. This approach is easily extensible for new feeds by simply creating new crawlers and ETL jobs. The other options are less efficient or suitable. The first option with multiple Lambdas and SQS queues introduces unnecessary complexity and latency. The second option using Fargate is viable but Glue is purpose-built for ETL, offering more integrated features like schema inference and job orchestration. The fourth option involving Athena and EMR is overly complex and expensive for this specific use case, as EMR clusters require more management and can be overkill for routine data transformations of this volume.