What should the solutions architect do to meet these requirements?
Create an Amazon S3 bucket to store the data. Configure the application to scan for new data in the bucket for processing.
Create an Amazon API Gateway endpoint to handle transmitted location coordinates. Use an AWS Lambda function to process each item concurrently.
Create an Amazon Simple Queue Service (Amazon SQS) queue to store the incoming data. Configure the application to poll for new messages for processing.
Create an Amazon DynamoDB table to store transmitted location coordinates. Configure the application to query the table for new data for processing. Use TTL to remove data that has been processed.
Explanations:
Storing data in an S3 bucket is a good option for storage, but polling for new data would add operational overhead. The application would still be overwhelmed during high traffic.
While API Gateway and Lambda can scale automatically, it might not handle large volumes of data effectively without additional throttling or queueing, and the loss of data could still occur.
Amazon SQS is a fully managed queue service that can handle high data throughput and scale automatically. It decouples the application from the data ingestion process, reducing the risk of data loss during traffic spikes.
Using DynamoDB for storing location data could work for small amounts of data, but it may become costly or inefficient at scale. Additionally, querying the table frequently could add operational overhead.
As I understand it, the answer is:
Create an Amazon Simple Queue Service (Amazon SQS) queue to store the incoming data. Configure the application to poll for new messages for processing.
I estimate that the answer is:
Create an Amazon Simple Queue Service (Amazon SQS) queue to store the incoming data. Configure the application to poll for new messages for processing.