Which solution will meet these requirements with the LEAST operational overhead?

Explanations:

This option uses Amazon API Gateway to handle HTTP requests efficiently, which is a fully managed service that automatically scales to accommodate incoming traffic. AWS Lambda provides serverless compute to process the data, which reduces operational overhead by eliminating the need to manage servers. Storing data in DynamoDB, a fully managed NoSQL database, further reduces operational tasks and allows for easy scaling and management. This architecture is suitable for future feature expansion, as it can integrate other AWS services seamlessly.

While using an Elastic Load Balancer (ELB) and EC2 instances can process the data, this option requires significant management overhead, including handling server maintenance, scaling, and configuration. Additionally, using Amazon S3 for storage is not optimal for real-time data retrieval and processing as needed for hourly energy consumption data. This architecture lacks the immediate scalability and operational efficiency of managed services.

This option also uses Amazon API Gateway and AWS Lambda, which is good, but it requires hosting a Microsoft SQL Server Express database on an EC2 instance. This adds operational overhead due to server management, updates, and maintenance. Moreover, running a relational database in EC2 limits scalability compared to fully managed database services like DynamoDB.

Similar to option B, this solution uses an ELB with an Auto Scaling group of EC2 instances, requiring significant management efforts for the servers. Using Amazon EFS introduces additional complexity, as it is primarily intended for file storage rather than structured data storage and retrieval. This option lacks the benefits of managed services that reduce operational overhead.