Stream Processing Paradigms

Welcome to Stream Processing Paradigms! In the previous lessons, we defined stream processing and compared it to batch processing. Now, let's explore the core models and approaches used to build real-time data applications.

Understanding these paradigms is crucial for designing efficient and robust systems that can handle continuous data flows.

The simplest paradigm is event-at-a-time processing. Here, each individual data event is processed as soon as it arrives, without waiting for other events.

This model is ideal for scenarios requiring immediate action or very low latency, like fraud detection or real-time alerts. It's often stateless, meaning it doesn't remember past events.