The question whether we need distributed stream processing can hardly be answered unambiguously. In general, everything depends on what features we actually need: scalability, fault tolerance, high availability, performance (latency or throughput) etc.

In any case, there are two ways to distribute stream processing tasks:

* [~Heavyweight] Use a cluster with multiple runners like [Spark Streaming](https://spark.apache.org/streaming/) or [Flink](https://flink.apache.org/)

* [~Lightweight] Do stream processing in applications, at the edge, on gateways or devices like [Bistro Streams](https://github.com/asavinov/bistro/tree/master/server) or [Kafka Streams](https://kafka.apache.org/documentation/streams/)

Normally, distributed stream processing requires also partitioning the data, e.g., by user or session ids, so that these isolated streams can be processed independently at different nodes.