Real-Time Analytics

Real-time analytics is the processing and analysis of data as it is generated — within milliseconds to seconds of an event occurring — to produce insights or trigger actions immediately. It contrasts with batch analytics, which processes accumulated data on a schedule (hourly, nightly).

Real-time analytics infrastructure combines event streaming (Kafka, Kinesis), stream processing (Flink, Spark Streaming), and OLAP databases optimized for low-latency queries (ClickHouse, Apache Druid, Pinot). AI applications in fraud detection, personalization, and operational monitoring require real-time analytics to act on events before they become irreversible. Latency requirements of under 100ms rule out most batch architectures entirely.