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Streaming describes continuous processing and transmission of events or data streams in real time. It enables low latency, continuous analytics and loosely coupled integration between producers and consumers and supports scaling, fault tolerance and stateful processing. Effective use requires architectural choices, operational practices and observability.
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Streaming transfers and processes data continuously so recipients can use individual events or ongoing results without waiting for a complete batch query.
Streaming developed from networking, multimedia delivery, and continuous-event processing. Distributed messaging systems such as Apache Kafka made the pattern accessible for decoupled business applications.
A producer writes a sequence of events, a service processes it, and consumers read it as needed. Storage, state, and delivery rules determine latency and reliability.
At its core, this means: Streaming transfers and processes data continuously so recipients can use individual events or ongoing results without waiting for a complete batch query.
It works by: A producer writes a sequence of events, a service processes it, and consumers read it as needed. Storage, state, and delivery rules determine latency and reliability.
Streaming connects decoupled producers and consumers with low latency. Architecture decisions cover delivery, retention, replay, and failure handling.
Streaming suits decoupled producers and consumers that exchange and process events continuously with low latency. Architecture decisions concern delivery, retention, replay, and failure handling.
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