Stream Processing
Stream processing is the continuous, real-time processing of data as it arrives, enabling near-instantaneous analysis and action on flowing data rather than waiting to batch it up.
Definition
Stream processing analyzes and transforms data continuously as it flows through a system, rather than waiting to collect a batch. Events arrive one at a time or in small windows (microseconds to seconds), and are processed, filtered, aggregated, or enriched in real-time. Stream processing enables immediate responses: fraud alerts within milliseconds, recommendation updates as users interact with your app, real-time dashboards, and operational analytics. Technologies like Apache Kafka, Apache Flink, and Spark Streaming power modern stream pipelines. Stream processing trades some simplicity and cost (compared to batch) for low latency and responsiveness.
How It Works
1. Source: Events arrive from systems (databases via CDC, message queues, APIs, sensors). 2. Ingest: A streaming platform (Kafka, Pulsar) buffers and distributes events. 3. Process: Stream processors apply stateless transformations (filter, map, enrich) or stateful aggregations (sum, join, windowed count). 4. Output: Results are written to analytics systems, caches, or operational databases. 5. Monitor: Latency and throughput are tracked; backpressure is managed.
When to Use It
Choose stream processing when you need low-latency insights (sub-second), when you want to trigger actions on events immediately, or when you're analyzing continuous data streams (IoT, clickstreams, transactions). Stream is more complex and expensive than batch, so batch is still right for scheduled reports. Many organizations use both: streaming for operational alerting, batch for historical reporting.
Last updated: Jun 17, 2026