Stream processing is the core component of event streaming, and it involves the continuous processing of real time data streams, including filtering, transformation, and data aggregation.
Event-driven architecture is a design pattern that focuses on the production, detection, and reaction to events. In an event-driven architecture, the system responds to real time events rather than waiting for batch processing.
Event streaming is highly scalable, allowing organizations to handle massive volumes of data in real time. It can handle large numbers of data sources, processing them in parallel and scaling resources up or down as needed.
Event streaming systems are designed to be fault-tolerant. They use techniques such as redundancy and replication to ensure that the system can continue to operate even if one or more components fail.