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.
Dean Wampler discusses the strengths and weaknesses of MapReduce, and the newer variants for big data processing: Pregel and Storm.
Nathan Marz discusses Storm concepts –streams, spouts, bolts, topologies-, explaining how to use Storms’ Clojure DSL for real-time stream processing, distributed RPS and continuous computations.
Nathan Marz explain Storm, a distributed fault-tolerant and real-time computational system currently used by Twitter to keep statistics on user clicks for every URL and domain.
David Syer and Mark Fisher on using Spring to develop concurrent and distributed apps, covering topics such as: asynchronous execution, intra-process, inter-process and inter-JVM communication.
Randy Shoup discusses the cloud programming model, covering topics such as state/statelessness, distribution, workload partitioning, cost and resource metering, automation, and deployment strategies.