Michael Nygard explores some of the available loopholes in the CAP theorem helping architects to engineer distributed systems that meet their needs.
Migration to Model Driven Engineering in the Development Process of Distributed Scientific Application Software
Daniel Rahon, Sébastien Schneider, Raphael Gayno, Jean Marc Gratien, Goulwen Le Fur present the process used in the development of distributed scientific applications at IFP Energies Nouvelles, France
Nathan Marz introduces Twitter Storm, outlining its architecture and use cases, and takes a look at future features to be made available.
Rob Lancaster explains the steps made by Orbitz in order to bridge the gap between their data in the data warehouse and the data in Hadoop.
Eli Collins introduces Hadoop: why it came about, the benefits it produces, its history, its architecture, use cases and applications.
Jason Bloomberg explains the architectural requirements for Cloud-based applications and how REST can be used to achieve elasticity in the cloud.
Dhruba Borthakur discusses the different types of data used by Facebook and how they are stored, including graph data, semi-OLTP data, immutable data for pictures, and Hadoop/Hive for analytics.
Amit Rathore describes the architecture of Zolodeck, a virtual relationship manager built on Clojure, Datomic, and Storm.
Yaniv Rodenski introduces Hadoop, then running Hadoop on Azure and the available tools and frameworks.
Mark Phillips discusses 3 types of distributed systems and how they run them at Basho: Computer Systems, Communities, and Companies.
Justin Sheehy discusses designing reliable distributed systems that can scale in order to deal with concurrency problems and the tradeoffs required by such systems.
Dean Wampler discusses the strengths and weaknesses of MapReduce, and the newer variants for big data processing: Pregel and Storm.