Stuart Williams takes a walk through the RTI architecture and explains how Spring performs at hundreds (and millions) of events/operations per second.
Ian Cooper takes a look at two architectural patterns: pipelines for dealing with streams of data effectively, and events which provide significant advantages for loose coupling.
Jafar Husain shows the Reactive Extensions (Rx) library which allows to treat events as collections, how Netflix uses Rx on the client and the server, allowing to build end-to-end reactive systems.
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 Netflix's new stream processing system that supports a reactive programming model, allows auto scaling, and is capable of processing millions of messages per second.
Terence Yim from Continuuity showcases a transactional stream processing system that supports full ACID properties without compromising scalability and high throughput.
Gabriel Gonzalez introduces TSAR (TimeSeries AggregatoR), a service for real-time event aggregation designed to deal with tens of billions of events per day at Twitter.
Jonas Bonér discusses four key traits of Reactive Apps: Event-Driven, Scalable, Resilient and Responsive, how they impact application design, how they interact, related technologies and techniques.
Todd Montgomery explains using WebSocket and reactive programming in an event driven RESTful architecture for the emerging IoT world.
Jonas Bonér discusses how the four traits of reactive apps -Event-Driven, Scalable, Resilient and Responsive- impact app design, how they interact, and their supporting technologies and techniques.
Greg Young discusses unexpected use cases and possible usages of the Event Store.