Spring’s journey on Data Integration started with Spring Integration. With its programming model, it provided a consistent developer experience to build applications that can embrace Enterprise Integration Patterns to connect with external systems such as, databases, message brokers, and among others.
Fast forward to the cloud-era, where microservices have become prominent in the enterprise setting. Spring Boot transformed the way how developers built Applications. With Spring’s programming model and the runtime responsibilities handled by Spring Boot, it became seamless to develop stand-alone, production-grade Spring-based microservices.
To extend this to Data Integration workloads, Spring Integration and Spring Boot were put together into a new project. Spring Cloud Stream was born.
With Spring Cloud Stream, developers can: * Build, test, iterate, and deploy data-centric applications in isolation. * Apply modern microservices architecture patterns, including composition through messaging. * Decouple application responsibilities with event-centric thinking. An event can represent something that has happened in time, to which the downstream consumer applications can react without knowing where it originated or the producer’s identity. * Port the business logic onto message brokers (such as RabbitMQ, Apache Kafka, Amazon Kinesis). * Interoperate between channel-based and non-channel-based application binding scenarios to support stateless and stateful computations by using Project Reactor’s Flux and Kafka Streams APIs. * Rely on the framework’s automatic content-type support for common use-cases. Extending to different data conversion types is possible.