Streaming Architectures for Real-Time Data
Streaming Architectures for Real-Time Data Real-time data streams help teams react quickly. A streaming architecture moves events from apps to dashboards and alerts with minimal delay. The goal is to process information as it arrives, not after it sits in a batch queue. Core patterns Publish–subscribe: producers publish events to topics and consumers subscribe as needed. Micro-batch streaming: small time windows balance latency and throughput. Change data capture: only the changes are sent, reducing noise and delay. These patterns work with durable tools such as Kafka, Kinesis, or Pulsar for the broker, and engines like Flink, Spark Structured Streaming, or Beam for processing. They support scalability and fault tolerance when the data flow grows. ...