From f1dbea98dcbe8941cb38dfb61db2516b93bd28be Mon Sep 17 00:00:00 2001 From: Sabby Anandan Date: Tue, 1 May 2018 11:10:13 -0700 Subject: [PATCH] Add SCSt's data integration journey Address review comments Resolves #1358 Resolves #1373 --- .../src/main/asciidoc/preface.adoc | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/spring-cloud-stream-core-docs/src/main/asciidoc/preface.adoc b/spring-cloud-stream-core-docs/src/main/asciidoc/preface.adoc index 30f4d80bd..f94db8d4e 100644 --- a/spring-cloud-stream-core-docs/src/main/asciidoc/preface.adoc +++ b/spring-cloud-stream-core-docs/src/main/asciidoc/preface.adoc @@ -1,3 +1,19 @@ +== A Brief History of Spring's Data Integration Journey + +Spring's journey on Data Integration started with https://projects.spring.io/spring-integration/[Spring Integration]. With its programming model, it provided a consistent developer experience to build applications that can embrace http://www.enterpriseintegrationpatterns.com/[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. https://projects.spring.io/spring-boot/[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. + == Quick Start You can try Spring Cloud Stream in less then 5 min even before you jump into any details by following this three-step guide.