Real-time analytics is the processing and analysis of data immediately as it is generated, producing insights and triggering actions with sub-second to low-second latency. It relies on stream-processing engines, in-memory computation, and windowed aggregation rather than batch ETL. It enables responsive dashboards, anomaly detection, and adaptive systems that act on fresh data.
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- Implementations use stream processors (Kafka Streams, Flink, materialised views) and in-memory stores to maintain continuously updated aggregates. Pushing computation to edge nodes reduces round-trip latency and bandwidth, supporting closed-loop adaptation in interactive and IoT systems where stale data would degrade responsiveness.