A solutions architect is designing an application to accept timesheet entries from employees on their mobile devices. Timesheets will be submitted weekly, with most of the submissions occurring on…

Deploying to Amazon EC2 On-Demand instances introduces significant operational overhead for server management, patching, and scaling compared to serverless alternatives, which conflicts with the requirement to minimize overhead.

While Amazon ECS can offer scalability, it still involves managing container clusters and underlying EC2 instances or Fargate tasks, which is not as operationally efficient or serverless as a Lambda/DynamoDB approach for the application backend.

Deploying the application frontend to an Amazon S3 bucket served by Amazon CloudFront provides a highly available, globally distributed, and scalable solution for static content with minimal operational overhead. Using AWS Lambda for backend logic and Amazon DynamoDB for timesheet submissions provides a fully serverless, automatically scaling, and highly available architecture for the application's transactional needs.

Amazon Redshift is a provisioned data warehouse that requires management and scaling, leading to higher operational overhead and potentially higher costs compared to the serverless Amazon Athena for periodic reporting on S3 data.

Storing timesheet submission data in Amazon S3 leverages its high durability, scalability, and cost-effectiveness for raw data. Using Amazon Athena allows payroll administrators to run ad-hoc SQL queries directly on the data in S3 for monthly reports without managing any infrastructure, while Amazon QuickSight provides serverless business intelligence and visualization capabilities, minimizing operational overhead for reporting.