Kinesis data ingestion

Hi AWS, there is a question:

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?

1. Publish data to Amazon Kinesis Data Streams, Use Kinesis Data Analytics to query the data.
2. Publish data to Amazon Kinesis Data Firehose with Amazon Redshift as the destination. Use Amazon Redshift to query the data.
3. 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.
4. 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.

Kinesis Data Streams for used for real-time ingestion whereas Kinesis Data Firehose is for near real-time so option (B) should be correct but the poll says option (A) is the right one because as per it "Redshift would lack real-time capabilities."

This is not true. Redshift could do real-time. Evidence: https://aws.amazon.com/blogs/big-data/real-time-analytics-with-amazon-redshift-streaming-ingestion/

Please suggest.