Streaming Data Processing

Streaming Data Processing is a computer science paradigm for continuously processing unbounded streams of data in real-time or near-real-time. In contrast to traditional batch processing, which operates on finite, stored datasets, this approach handles data "in motion," performing computations such as filtering, aggregation, and analysis as individual data records are generated or received from sources like IoT sensors, financial tickers, or social media feeds. This method is essential for applications that require immediate insights and low-latency responses, such as fraud detection, system monitoring, and real-time personalization.

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2. Core Concepts in Stream Processing