Which solution will meet these requirements with the LEAST operational overhead?
Use Amazon API Gateway with AWS Lambda functions to receive the data from the sensors, process the data, and store the data in an Amazon DynamoDB table.
Use an Elastic Load Balancer that is supported by an Auto Scaling group of Amazon EC2 instances to receive and process the data from the sensors. Use an Amazon S3 bucket to store the processed data.
Use Amazon API Gateway with AWS Lambda functions to receive the data from the sensors, process the data, and store the data in a Microsoft SQL Server Express database on an Amazon EC2 instance.
Use an Elastic Load Balancer that is supported by an Auto Scaling group of Amazon EC2 instances to receive and process the data from the sensors. Use an Amazon Elastic File System (Amazon EFS) shared file system to store the processed data.
Explanations:
This option utilizes Amazon API Gateway and AWS Lambda, which are fully managed services that handle the incoming HTTP requests with minimal operational overhead. Lambda can automatically scale with the workload and DynamoDB provides a scalable NoSQL database solution for storing energy consumption data, making it a flexible choice for future feature additions.
Using an Elastic Load Balancer with an Auto Scaling group of EC2 instances introduces higher operational overhead due to the need to manage and maintain the EC2 instances and load balancer. Additionally, storing data in Amazon S3 is not optimal for querying and processing the energy consumption data, which is better suited for a database solution.
While this option uses managed services (API Gateway and Lambda), it relies on Microsoft SQL Server Express on an EC2 instance for storage. This introduces operational overhead since managing EC2 instances and a database installation requires more maintenance than fully managed services.
Similar to option B, this option uses an Elastic Load Balancer with EC2 instances, which adds significant operational overhead. Additionally, using Amazon EFS for storage is less efficient for structured data and querying compared to using a managed database service, which complicates future scalability and feature integration.
I rank that the answer is:
Use Amazon API Gateway with AWS Lambda functions to receive the data from the sensors, process the data, and store the data in an Amazon DynamoDB table.
I calculate that the answer is:
Use Amazon API Gateway with AWS Lambda functions to receive the data from the sensors, process the data, and store the data in an Amazon DynamoDB table.