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A company ingests and processes streaming market data.

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A company ingests and processes streaming market data. The data rate is constant. A nightly process that calculates aggregate statistics is run, and each execution takes about 4 hours to complete. The statistical analysis is not mission critical to the business, and previous data points are picked up on the next execution if a particular run fails.

The current architecture uses a pool of Amazon EC2 Reserved Instances with 1-year reservations running full time to ingest and store the streaming data in attached Amazon EBS volumes. On-Demand EC2 instances are launched each night to perform the nightly processing, accessing the stored data from NFS shares on the ingestion servers, and terminating the nightly processing servers when complete. The Reserved Instance reservations are expiring, and the company needs to determine whether to purchase new reservations or implement a new design.

Which is the most cost-effective design?

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 nightlyprocessing 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 Load Balancer. Use AWS Batch with SpotInstances 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 Lambdafunction to run nightly to query Amazon Redshift to generate the daily statistics.

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