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Stream processing enables organizations to conduct real-time data analytics for immediate decision-making. This method is often used in big data applications to process and analyze large volumes of data efficiently.
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Stream processing continuously handles arriving events so that insights and reactions can be produced with low delay.
The approach grew from processing continuous data streams and was advanced by distributed systems and big-data platforms. Apache Kafka is a widely used open-source system for storing and distributing such event streams.
Treat data as a continuous sequence of events. Producers write events, processors transform or aggregate them, and consumers react; time, ordering, and state management determine the semantics.
At its core, this means: Stream processing continuously handles arriving events so that insights and reactions can be produced with low delay.
It works by: Treat data as a continuous sequence of events. Producers write events, processors transform or aggregate them, and consumers react; time, ordering, and state management determine the semantics.
Stream processing evaluates events as they arrive, for example for fraud detection or monitoring. Establish the required latency, ordering, and state management.
Stream processing matters when events must be evaluated as they arrive, for example for fraud detection, monitoring, or immediate reactions. The design must establish the latency, ordering, and state management the use case requires.
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