Sean Cribbs compares ACID with BASE, explaining the virtues and tradeoffs of eventually consistent systems and what developers should know in order to feel comfortable working with EC systems.
Costin Leau discusses Big Data, current available tools for dealing with it, and how Spring can be used to create Big Data pipelines.
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.