Real-time data processing involves handling and analyzing data as it is generated or received, enabling immediate processing and response without significant delays. It is essential in various applications, including finance, where quick decisions based on real-time data are crucial.

In the context of real-time data processing, three fundamental concepts are event-driven architecture, data streaming, and latency.

Event-driven architecture is a design pattern where the flow of data and processing is driven by events or triggers. Events can be actions, changes in data, or external signals. In real-time data processing, event-driven architecture allows for the handling of data as soon as events occur, ensuring timely and accurate processing.

Data streaming is the continuous flow of data from sources to processing systems. Streaming data can be generated by various sources, such as IoT devices, sensors, or applications. Real-time data processing involves efficiently ingesting, processing, and analyzing streaming data to extract valuable insights and make informed decisions.

Latency refers to the time it takes for data to travel from its source to the processing system and for the corresponding results to be generated. In real-time data processing, low latency is crucial to ensure that data is processed and analyzed in near real-time, enabling timely actions and responses.

Let's dive deeper into each of these concepts and understand their significance in real-time data processing.