The correct answer is: Send the incoming data to Amazon Kinesis Data Streams and use Kinesis Data Analytics to run near-real-time queries on the stream.

Amazon Kinesis Data Streams is a high-throughput, durable, and scalable streaming service designed to capture real-time data from sources like EC2. It automatically replicates data across multiple Availability Zones and can retain it for up to 365 days, reducing the risk of data loss during EC2 failures or reboots. Kinesis Data Analytics provides built-in support for running SQL-based queries on streaming data, enabling near-real-time insights with low latency and minimal operational overhead.

The option Write the data to an EC2 instance store, then forward it to Amazon Kinesis Data Firehose with Amazon S3 as the destination. Use Amazon Athena to query the stored data is not suitable due to the volatility of instance store storage, which is lost on reboot. This setup does not support near-real-time querying and introduces higher data loss risk.

The option Publish the data to Amazon Kinesis Data Firehose and configure Amazon Redshift as the destination. Query the data using Redshift introduces latency due to Firehose buffering and Redshifts batch-based processing. This solution is better suited for analytics over aggregated data, not for near-real-time streaming queries.

The option Store the data on an EBS volume attached to the EC2 instance, then stream the data to Amazon ElastiCache for Redis and use Redis pub/sub to query it is not ideal. Redis is optimized for fast, temporary caching rather than durable storage or querying structured data. It lacks native support for complex querying or stream processing like Kinesis Analytics.

This question is important because it evaluates your understanding of real-time data ingestion, durability, and analysis at scale. When high-volume ingestion with near-instant querying and minimal data loss is required, Kinesis Data Streams combined with Kinesis Data Analytics is the most AWS-native, scalable, and resilient solution.

A useful trick is to match requirements for real-time querying with streaming analytics services like Kinesis, and to avoid solutions involving EC2-local storage or batch-only analytics services when the scenario demands low-latency, durable, and scalable data processing.

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