Chris Richardson discusses an event-driven microservice architecture, it’s benefits and drawbacks and how Spring Boot can help, implementing business logic using domain models written in Scala.
Alberto Brandolini discusses how the Theory of Constraints, Kanban, CQRS, Domain-Driven Design, EventStorming and UX blend together to solve the real problems in software development.
Udi Dahan takes a look at why different kinds of pub/sub need to be used for specific domains like healthcare, finance, “internet of things”, and some kinds of retail.
Glenn Renfro discusses how to create an application with a scheduler that will retrieve data from a web service, cleanse and emit the data via MQTT, by utilizing Spring Boot and Spring Integration.
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.
Husain shows the Reactive Extensions (Rx) library which allows one to treat events as collections, how Netflix uses Rx on the client and the server, allowing it 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.