What is stream processing primarily concerned with?

Stream processing is primarily concerned with performing operations on events as they happen. This approach allows for real-time data processing, enabling organizations to react to events instantaneously as they occur. By continuously processing streams of data, stream processing can analyze and respond to incoming information, facilitating scenarios such as real-time analytics, fraud detection, and monitoring systems.

This stands in contrast to methods that focus on static data manipulation or historical data storage. While transforming static data into dynamic forms has its importance, it lacks the immediacy and event-driven nature of stream processing. Similarly, storing historical data in offline databases pertains more to data warehousing and batch processing rather than the continuous and immediate aspect of stream processing. Finally, the creation of backups for stateful applications does not align with the core principles of stream processing, which are more about immediate computation and response rather than data preservation or redundancy.