Stream processing is a method of processing large amounts of data in real-time, as the data is being generated or received, rather than processing it in batches. This is useful in scenarios where data is constantly being generated and needs to be analyzed or acted upon immediately, such as in financial transactions, sensor data, or social media feeds.
Stream processing systems typically consist of a pipeline of stages, where each stage performs a specific operation on the data, such as filtering, transforming, or aggregating. The data flows through the pipeline in a continuous stream, and each stage can process the data in parallel to improve performance.
There are several open-source and commercial stream processing frameworks and technologies available, such as Apache Kafka, Apache Storm, Apache Flink, Apache Samza, and Apache Spark Streaming, each with different set of features, capabilities and performance characteristics. These frameworks and technologies can be used to build a stream processing pipeline and handle large amounts of data in real-time and make real-time decisions based on the data.