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 m 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 m an Amazon Elastic Block Store (Amazon EBS) volume Publish data to Amazon ElastiCache tor Red Subscribe to the Redis channel to query the data

Answer: A