Ben Stopford examines tools, mechanisms and tradeoffs that allow a data architecture to scale, from disk formats to fully blown architectures for real-time storage, streaming and batch processing.
Arun Gupta explains how to do Java EE 7 development with Eclipse, leveraging the new APIs - WebSocket, Batch, JSON Processing, and Concurrency Utilities.
Owen Rubel discusses the benefits of API abstraction: easier externalization, synchronization and sharing, reloading the API config on the fly, DRY'r code, batching, reduced throughput and much more.
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.