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.
Vinicius Carvalho talks about the role of a centralized Schema repository, and how can we work with different data models and protocols to achieve schema evolution.
Michael Minella and Glenn Renfro introduce Spring Cloud Task providing capabilities for building short lived, cloud-native microservices, as well as look at example applications.
Mark Pollack introduces Spring Cloud Data Flow enabling one to create pipelines for data ingestion, real-time analytics and data import/export, demoing apps that are deployed onto multiple runtimes.
Marcin Grzejszczak shows how to use the Spring Cloud Contract Verifier to stub HTTP / Messaging collaborators, faking microservices with stubs that were tested against their producer.
Matthew Mark Miller discusses Kubernetes’ primitives and microservices patterns on top of them, including rolling deployments, stateful services and adding behaviors via sidecars.
Chase Aucoin explains using Microsoft Service Fabric to create microservices, demoing how to migrate existing services to Service Fabric.
Kevin Hoffmann and Chris Umbel discuss building .NET microservices and deploying them to the Spring Cloud.
Marcin Grzejszczak and Reshmi Krishna describe how to do distributed tracing with Spring Cloud Sleuth and Zipkin, demoing incorporating these technologies into an existing stock trading application.
Adrian Cole overviews how to debug latency problems using call graphs created by Zipkin, taking a look at the ecosystem, including tools to trace Ruby, C#, Java and Spring Boot apps.