Scott Andreas discussing creating fault tolerant distributed applications, and demoes Ordasity, a framework for building self-organizing systems with services.
Hairong Kuang explains how Facebook uses HDFS to store and analyze over 100PB of user log data.
Pieter Hintjens explains how to use contracts and rapid iterative design cycles to architect large-scale distributed systems with ZeroMQ.
Nikita Ivanov shows adding real-time capabilities to Hadoop through a demo application streaming word counting on a 2-nodes cluster.
Kathleen Ting details 8 misconfigurations that can bring ZooKeeper down.
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.