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ABCD has developed a sensor intended to be placed inside of people's shoes, monitoring the number of steps taken every day. ABCD is expecting thousands of sensors reporting in every minute and hopes to scale to millions by the end of the year. A requirement for the project is it needs to be able to accept the data, run it through ETL to store in warehouse and archive it on Amazon Glacier, with room for a real-time dashboard for the sensor data to be added at a later date. What is the best method for architecting this application given the requirements? Choose the correct answer:
AUse Amazon Cognito to accept the data when the user pairs the sensor to the phone, and then have Cognito send the data to Dynamodb. Use Data Pipeline to create a job that takes the DynamoDB tablee and sends it to an EMR cluster for ETL, then outputs to Redshift and S3 while, using S3 lifecycle policies to archive on Glacier.
BWrite the sensor data directly to a scaleable DynamoDB; create a data pipeline that starts an EMR cluster using data from DynamoDB and sends the data to S3 and Redshift.
CWrite the sensor data to Amazon S3 with a lifecycle policy for Glacier, create an EMR cluster that uses the bucket data and runs it through ETL. It then outputs that data into Redshift data warehouse.
DWrite the sensor data directly to Amazon Kinesis and output the data into Amazon S3 creating a lifecycle policy for Glacier archiving. Also, have a parallel processing application that runs the data through EMR and sends to a Redshift data warehouse.