Introduction to Stream Processing

Stream processing is a big data architecture that allows for real-time analysis of data, providing crucial insights within milliseconds and operating asynchronously without waiting for responses between the data source and processing. It contrasts with batch processing, which handles large data volumes in groups sequentially, usually at the end of a business cycle. Stream processing infrastructure includes real-time data sources, data processing frameworks like Apache Flink and Kafka, and streaming analytics users who act upon processed data for applications such as fraud detection, IoT data management, and personalized advertising. Companies like Netflix and Amazon use stream processing to handle vast amounts of log data, providing valuable insights and enabling immediate responses to issues or opportunities. The framework supports various use cases, such as real-time analytics, fraud detection, IoT data management, and personalized marketing, by continuously processing data and generating actionable reports or alerts.

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