Amazon Kinesis Data Streams for Multiple Independent Consumers

Quick Answer

Kinesis Data Streams fits this scenario because of how it separates producers from consumers: once click events are written into the stream, any number of independent consumer applications can read from it in parallel without competing for or removing the data, since each consumer tracks its own position in the stream rather than deleting records as they're read. That's the key property being tested, a high-volume, real-time event source that more than one downstream system needs to process on its own terms. The shard-level throughput characteristics reinforce why this scales well for that use case: each shard supports a defined number of read transactions per second and a defined data read rate, so the stream can be provisioned to handle both the incoming volume and the number of consumers reading it. This is different from a model where a single message is claimed and processed once, which works for typical queue-style workloads but breaks down as soon as more than one independent system needs to see every event. When a scenario describes streaming or event data that has to be processed by several separate consumers rather than handed off to just one worker, that requirement for repeatable, independent, parallel reads is the signal to reach for Kinesis Data Streams over a single-consumer queuing service.