Which solution provides near-real-time data querying that is scalable with minimal data loss?

A company is using a fleet of Amazon EC2 instances to ingest data from on-premises data sources. The data is in JSON format and ingestion rates can be as high as 1 MB/s. When an EC2 instance is rebooted, the data in-flight is lost. The company’s data science team

wants to query ingested data in near-real time.

Which solution provides near-real-time data querying that is scalable with minimal data loss?
A . Publish data to Amazon Kinesis Data Streams. Use Kinesis Data Analytics to query the data.
B . Publish data to Amazon Kinesis Data firehose with Amazon Redshift as the destination.
Use Amazon Redshift to query the data.
C . Store ingested data in an EC2 instance store Publish data to Amazon Kinesis Data Firehose with Amazon S3 as the destination. Use Amazon Athena to query the data.
D . Store ingested data in an Amazon Elastic Block Store (Amazon EBS) volume. Publish data to Amazon ElastiCache for Redis. Subscribe to the Redis channel to query the data.

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Answer: C