Which combination of AWS services will meet these requirements MOST cost-effectively?

Explanations:

Amazon EC2 is a compute service that can run applications in the cloud. While it can be used for processing data, it requires more management and operational overhead compared to managed services. It is not the most cost-effective solution for near-real-time data processing.

AWS Batch is designed for batch processing jobs, making it suitable for scenarios with large data volumes processed at scheduled intervals, but it is not suited for near-real-time streaming data processing. It doesn’t address the requirement for timely insights from streaming data.

Amazon Simple Queue Service (Amazon SQS) is a fully managed message queuing service that helps decouple microservices and applications. While it can be part of a data processing architecture, it does not directly process or analyze streaming data in real-time, and thus is not a primary choice for this requirement.

Amazon Kinesis Data Firehose is a fully managed service that automatically scales to match the throughput of incoming data and can be used to load streaming data into data stores. It allows for near-real-time processing of clickstream data with minimal operational overhead, making it a cost-effective choice for the requirements.

Amazon Kinesis Data Analytics allows for processing streaming data in real-time using standard SQL queries. It can provide immediate insights into the data being ingested, making it ideal for near-real-time analytics with low operational overhead. This makes it a cost-effective solution for the company’s needs.