Stream Processing

Stream processing is the continuous computation of results on data records as they arrive, rather than accumulating them for batch processing. Each record is processed individually or in small windows, enabling near-real-time insights and actions.

Stream processing is used when latency matters: detecting a hotel price change within seconds, triggering a deal alert the moment a product drops below a threshold, or updating a live dashboard with property listing activity as it happens. Frameworks like Apache Kafka, Apache Flink, and AWS Kinesis are designed for this.

Building stream processing pipelines is more complex than batch processing - you must handle out-of-order events, exactly-once delivery guarantees, and stateful computation across time windows. For most API-based data integrations, batch processing is simpler and sufficient; stream processing is justified when sub-minute latency is a hard requirement.