Vaclav Petricek discusses how to train models, architect and build a scalable system powered by Storm, Hadoop, Spark, Spring Boot and Vowpal Wabbit that meets SLAs measured in tens of milliseconds.
Tal Weiss explores five crucial Java techniques for distributed debugging and some of the pitfalls that make bug resolution much harder, and can even lead to downtime.
Dan Woods discusses the approach to developing a scalable enterprise architecture, and demonstrates implementations based on the variety of technologies available from the Groovy ecosystem.
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.
Ryan Cromwell introduces Elixir, a , functional distributed meta programming language inspired by Ruby and compiling to Erlang VM, covering pattern matching, pipelines and tail-call recursion.
Jamie Allen describes three patterns using Akka actors: handling a lack of guaranteed delivery, distributing tasks to worker actors and implementing distributed workers in an Akka cluster.
Jonas Bonér, Francesco Cesarini discuss the evolution of distributed concurrent thinking along with the problems it has to solve and the toolchains created along the way.
Heather Miller presents attempts at better supporting distributed programming in Scala, including a new fast pickling framework, as well as Spores - composable pieces of mobile functional behaviour.
Brenden Matthews describes the infrastructure built at Airbnb using Mesos in order to support Hadoop and Storm.
Nathan Marz shares lessons learned building Storm, an open-source, distributed, real-time computation system.
Nathan Marz introduces Twitter Storm, outlining its architecture and use cases, and takes a look at future features to be made available.
Amit Rathore describes the architecture of Zolodeck, a virtual relationship manager built on Clojure, Datomic, and Storm.