realtimestreaming

RealTimeStreaming refers to the continuous transmission and processing of data or media with low end-to-end latency, enabling near-immediate delivery as it is generated. It covers both real-time data streams (events, telemetry, logs) and real-time media streams (live audio and video).

RealTimeStreaming systems seek low latency, continuous data flow, ordered delivery, scalability, and fault tolerance. They must cope with backpressure, out-of-order events, and data integrity under fluctuating networks, while staying responsive across different workloads and traffic patterns.

Typical architecture includes producers, a streaming backbone or broker, and consumers or processing layers. Data streaming uses systems like Kafka, MQTT, or AMQP, with stream processing engines such as Flink, Spark Structured Streaming, or Storm. Media streaming involves encoders, segmenters, and delivery networks.

Common protocols vary by domain. Data streaming relies on message buses and schemas (Kafka, Avro, Protobuf); media streaming uses RTMP, RTSP, HLS, DASH, WebRTC. Latency targets range from sub-second for interactive apps to seconds for dashboards.

Applications include finance (live quotes, fraud detection), IoT telemetry, live analytics, online gaming, live video broadcasting, and surveillance. Challenges cover ordering guarantees, exactly-once semantics, schema evolution, security, privacy, and handling burst traffic while preserving quality.