Simon Marlow explains how to use Haxl to automatically batch and overlap requests for data from multiple data sources.
Michael Minella uses Spring XD and Spring Batch to orchestrate the full lifecycle of Hadoop processing and uses Apache Mahout to provide the audience with the recommendation processing.
Gunnar Hillert and Chris Schaefer examine various scalability options in order to improve the robustness and performance of the Spring Batch applications.
Josh Long and Phillip Webb present what Spring Boot is, why it's turning heads, why you should consider it for your next application and how to get started.
Thomas Risberg introduces the Spring for Apache Hadoop project and discusses integration with Spring XD, batch jobs and external data sources.
Jayesh Thakrar shows what can be done with irb, how to exploit JRuby-Java integration, and demonstrates how the Shell can be used in Hadoop streaming to perform complex and large volume batch jobs.
Todd Montgomery discusses messaging: application level batching, UDP datagram size’s impact on performance, sendmmsg/recvmmsg, implementing asynchronous calls.
Gunnar Hillert and Gary Russell introduce Spring Integration and Spring Batch, how they differ, their commonalities, and how you can use them together.
Wayne Lund introduces Java Batch JSR-352 explaining the domain and job specification languages used, the programming model and the runtime specification of the standard.
John Davies examines Visa’s architecture and shows how enterprises have architected complex integrations incorporating Hadoop, memcached, Ruby on Rails, and others to deliver innovative solutions.
David Syer and Mark Fisher on using Spring to develop concurrent and distributed apps, covering topics such as: asynchronous execution, intra-process, inter-process and inter-JVM communication.
Dave Syer and Mark Fisher demo using Spring Batch and Integration for real life situations where automation can save both operators and developers a lot of time by running automated batch jobs.