Batch processing runs a finite job over a bounded input, while stream processing runs continuously over data that may be unbounded. Batch is often selected when results can wait for a schedule or a complete input; streaming is selected when the system must update results or trigger actions as events arrive. These are execution models, not rigid performance tiers: either can handle large volumes, and modern systems such as Apache Beam, Apache Flink, and Spark expose APIs that blur the old boundary.

The design question is therefore broader than “throughput or speed.” Teams must specify acceptable end-to-end latency, completeness, event-time behavior, late-data policy, state size, failure recovery, delivery semantics, replay needs, sink behavior, operating cost, and staffing. A faster engine does not make a result correct if events are duplicated, delayed, out of order, or written non-atomically to a sink.