From 2970b3d57692baf6a1632cae75be0be332ffb6d8 Mon Sep 17 00:00:00 2001 From: pw Date: Wed, 5 Dec 2018 12:16:23 -0500 Subject: [PATCH] Updated formatting to properly show bullet points in README (#1545) * updated formatting * Update README.adoc --- README.adoc | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/README.adoc b/README.adoc index bee3ca144..aca9c1f57 100644 --- a/README.adoc +++ b/README.adoc @@ -23,12 +23,13 @@ Fast forward to the cloud-era, where microservices have become prominent in the 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. + + * 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 @@ -405,4 +406,4 @@ added after the original pull request but before a merge. if you are fixing an existing issue please add `Fixes gh-XXXX` at the end of the commit message (where XXXX is the issue number). -// ====================================================================================== \ No newline at end of file +// ======================================================================================