The critical part of becoming a data-centric business is to act on real-time events as and when they happen. Event-streams and its "state" are at the core of it - repeating it consistently and continuously is the key.
For example, stateful stream processing brings tremendous new opportunities for businesses, who with the real-time insights can interact with their customers in more meaningful ways. Kafka Streams APIs provide the primitives to interact with distributed data sets. As an event-driven microservice framework, Spring Cloud Stream provides the primitives to build cloud-native streaming applications with either imperative or functional programming models. By combining the both, we can create stateful streaming solutions to be orchestrated as Spring Boot applications in modern platforms such as Kubernetes or Cloud Foundry.
Given this flexibility, businesses can scale, upgrade, rollback, or continuously deliver data-centric business functions seamlessly. In this talk, we will explore how Spring Cloud Stream and Kafka Streams can support Event Sourcing and CQRS patterns.
Lastly, we will walk-through a practical approach to apply cloud-native patterns for data-intensive applications using Spring Cloud Data Flow and Spring Cloud Skipper.