Real-Time Analytics

Real-time analytics refers to the ability to process, analyze, and act on data as soon as it is generated—within milliseconds to seconds rather than hours or days. This involves streaming data architectures (Apache Kafka, Apache Flink, Amazon Kinesis), in-memory databases, and event-driven processing. Use cases include fraud detection, real-time pricing, live dashboards, IoT monitoring, and operational alerting. Real-time analytics systems must handle high throughput, low latency, and continuous data streams while maintaining accuracy. The trade-off is typically between freshness (how recent the data is) and completeness (whether all data has been processed).