The speakers discuss the organizational structure and communication and development strategies and tools to allow teams to work in parallel without drowning in process overhead and coordination costs.
Mike Gehard takes the journey from monolith to microservices.
Christian Posta explains building microservices with Spring, Spring Cloud, and Netflix OSS and running them on Docker and Kubernetes.
Eric Bottard and Ilayaperumal Gopinathan discuss easy composition of microservices with Spring Cloud Data Flow.
Katie Mooney, Dillon Woods and Cahlen Humphreys point out key differences in Spring XD that have been resolved in Spring Cloud Data Flow.
Rajini Sivaram talks about Kafka and reactive streams and then explores the development of a reactive streams interface for Kafka and the use of this interface for building robust applications.
Marius Bogoevici demonstrates how to create complex data processing pipelines that bridge the big data and enterprise integration together and how to orchestrate them with Spring Cloud Data Flow.
Micahel Klishin talks about how one can troubleshoot a distributed service-oriented system, focusing on Java, Spring, and RabbitMQ.
Lawrence Spracklen creates a machine learning model leveraging data within MPP databases such as Apache HAWQ or Greenplum integrated with Chorus and then deploying this as a microservice on PCF.
Fred Melo introduces Spring Cloud Stream from a Data Microservices perspective.
Gary Russell takes a look at the features of the spring-kafka project as well as the new version (2.0) of spring-integration-kafka which is now based on the Spring for Apache Kafka project.
Thomas Risberg discusses developing big data pipelines with Spring, focusing around the code needed and he also covers how to set up a test environment both locally and in the cloud.