A company ingests and processes streaming market data. The data rate is constant. A nightly process that calculates aggregate statistics takes 4 hours to complete. The statistical analysis is not critical to the business, and data points are processed during the next iteration if a particular run fails. The current architecture uses a pool of Amazon EC2 Reserved Instances with 1-year reservations. These EC2 instances run full time to ingest and store the streaming data in attached Amazon Elastic Block Store (Amazon EBS) volumes. A scheduled script launches EC2 On-Demand Instances each night to perform the nightly processing. The instances access the stored data from NFS shares on the ingestion servers. The script terminates the instances when the processing is complete. The Reserved Instance reservations are expiring. The company needs to determine whether to purchase new reservations or implement a new design. Which solution will meet these requirements MOST cost-effectively? A. Update the ingestion process to use Amazon Kinesis Data Firehose to save data to Amazon S3. Use a scheduled script to launch a fleet of EC2 On-Demand Instances each night to perform the batch processing of the S3 data. Configure the script to terminate the instances when the processing is complete. B. Update the ingestion process to use Amazon Kinesis Data Firehose to save data to Amazon S3. Use AWS Batch with Spot Instances to perform nightly processing with a maximum Spot price that is 50% of the On-Demand price. C. Update the ingestion process to use a fleet of EC2 Reserved Instances with 3-year reservations behind a Network LoadBalancer. Use AWS Batch with Spot Instances to perform nightly processing with a maximum Spot price that is 50% of the On-Demand price. D. Update the ingestion process to use Amazon Kinesis Data Firehose to save data to Amazon Redshift. Use Amazon EventBridge to schedule an AWS Lambda function to run nightly to query Amazon Redshift to generate the daily statistics.