What is stream processing?

Stream processing is a method of handling data continuously and in real time as it is generated, rather than processing batches of stored data after a delay. It focuses on analyzing and acting on data immediately, often using tools designed to handle high-throughput, low-latency scenarios. This approach is useful when timely insights are critical, such as monitoring systems, fraud detection, or live user interactions. Unlike batch processing, which operates on fixed datasets, stream processing deals with unbounded data streams that have no predefined start or end.

A key aspect of stream processing is its architecture. Data is ingested from sources like sensors, applications, or logs and processed incrementally as it arrives. Tools like Apache Kafka, Apache Flink, or AWS Kinesis are commonly used to manage these workflows. For example, a fraud detection system might analyze credit card transactions in real time, flagging suspicious patterns as they occur. Processing involves operations like filtering, aggregating, or transforming data—such as calculating rolling averages, detecting anomalies, or enriching events with additional context. Windowing techniques (e.g., tumbling or sliding windows) are often applied to group events into manageable chunks for analysis, like counting website clicks per minute.

Developers use stream processing to build responsive systems that react to events as they happen. Common use cases include real-time dashboards, alerting systems, or dynamically adjusting pricing based on demand. Challenges include handling out-of-order data, ensuring fault tolerance, and scaling to handle variable workloads. For instance, a logistics company might track delivery trucks using GPS data streams to optimize routes instantly. Stream processing requires careful design to balance latency, accuracy, and resource usage, but it enables applications that would be impractical with slower, batch-oriented methods.