Trisha Gee uses Java 8 streams and lambdas to build an app consuming a real-time feed of high velocity data, using services to make sense of the data, and presenting it in a JavaFX dashboard.
Small sessions on: Deterministic testing in a non-deterministic world. Hash Spreads and Probe Functions. Typesafe Config on Steroids. Real-Time Distributed Event-Driven Computing at Credit Suisse.
Matt Ranney explains the Uber architecture overall, with a focus on the dispatch systems, the geospatial index, handling failure, and dealing with the distributed traveling salesman problem.
This session explores the power of Spring XD in the context of the Internet of Things (IoT).
Eugene Mandel discusses challenges of conforming data sources and compares processing stacks: Hadoop+Redshift vs Spark, showing how the technology drives the way the problem is modeled.
Randy Shoup tells war stories from Google and eBay focusing on how to scale code, infrastructure, performance, and operations, along with hard-won lessons learned in scaling them.
Eugene Dvorkin provides an introduction to Storm framework, explains how to build real-time applications on top of Storm with Groovy, how to process data from Twitter in real-time, etc.
Garrett Wampole describes an experimental methodology of applying Enterprise Integration Patterns to the near real-time processing of surveillance radar data, developed by MITRE.
Neha Narkhede of Kafka fame shares the experience of building LinkedIn's powerful and efficient data pipeline infrastructure around Apache Kafka and Samza to process billions of events every day.
The authors discuss patterns and technologies needed to scale large enterprise mobile systems, covering handling network connectivity, data reliability and real-time communication.
Brian Degenhardt discusses lessons that Twitter learned managing a high rate of change and complexity, and how those can be applied anywhere.