A company provides an incentive to users who are physically active. The company wants to determine how active the users are by using an application on their mobile devices to track the number of steps they take each day. The company needs to ingest and perform near-real-time analytics on live data. The processed data must be stored and must remain available for 1 year for analytics purposes.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon Cognito to write the data from the application to Amazon DynamoDB. Use an AWS Step Functions workflow to create a transient Amazon EMR cluster every hour and process the new data from DynamoDB. Output the processed data to Amazon Redshift for analytics. Archive the data from Amazon Redshift after 1 year.
B. Ingest the data into Amazon DynamoDB by using an Amazon API Gateway API as a DynamoDB proxy. Use an AWS Step Functions workflow to create a transient Amazon EMR cluster every hour and process the new data from DynamoDB. Output the processed data to Amazon Redshift to run analytics calculations. Archive the data from Amazon Redshift after 1 year.
C. Ingest the data into Amazon Kinesis Data Streams by using an Amazon API Gateway API as a Kinesis proxy. Run Amazon Kinesis Data Analytics on the stream data. Output the processed data into Amazon S3 by using Amazon Kinesis Data Firehose. Use Amazon Athena to run analytics calculations. Use S3 Lifecycle rules to transition objects to S3 Glacier Flexible Retrieval after 1 year.
D. Write the data from the application into Amazon S3 by using Amazon Kinesis Data Firehose. Use Amazon Athena to run the analytics on the data in Amazon S3. Use S3 Lifecycle rules to transition objects to S3 Glacier Flexible Retrieval after 1 year.