Stream 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.

Streaming Data Processing with Apache Kafka and KSQL is a powerful paradigm for analyzing data in real-time, leveraging Apache Kafka as the distributed, fault-tolerant event streaming platform to ingest and store continuous data flows. Layered on top, KSQL (now ksqlDB) provides an interactive SQL interface that allows developers and analysts to filter, transform, aggregate, and join these data streams on the fly, without writing complex application code. This combination simplifies the creation of real-time applications, such as monitoring dashboards, anomaly detection systems, and dynamic pricing engines, by making the powerful capabilities of stream processing accessible through familiar SQL syntax.