Data
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.
Related terms
- Event Streaming — Event streaming is the continuous capture, transmission, and processing of event records in real time through a distributed log — Apache Kafka being the dominant platform.
- Event-Driven Automation — Event-driven automation is an architecture in which business processes are triggered by real-time events — a form submitted, a payment received, a threshold crossed, a message received — rather than by scheduled batch jobs or manual initiation.
- Fraud Detection (AI) — AI fraud detection uses ensemble models — rule-based screens, classical machine learning, and large language models — each catching what the previous layer missed.