Storm Real-Time

Apache Storm is a distributed stream processing framework for real-time analytics on unbounded data streams. Unlike batch systems (Hadoop) that process periodic snapshots, Storm processes events continuously as they arrive, delivering results with sub-second latency. Topologies (composed of spouts and bolts) define data flow: spouts inject events, bolts process them, and output to sinks. Storm is used for fraud detection, real-time recommendations, clickstream analysis, and any use case requiring immediate insight into streaming data. It's mature, battle-tested, but less trendy than newer systems (Flink, Kafka Streams). Real-time processing is increasingly critical for business intelligence and operational systems. Storm is one of the most deployed streaming systems; many production systems still run it. Demand for Storm expertise exceeds supply, especially for legacy system maintenance. For architects and engineers working on real-time pipelines, Storm knowledge is valuable. Salaries are competitive ($160-230k USD senior), especially for experienced operators. Understanding Storm also teaches streaming concepts applicable to newer systems.