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.
Parand Tony Darugar overviews Hadoop, its processing model, the associated ecosystem and tools, discussing some real-life uses of Hadoop for analyzing and processing large amounts of data.
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.
Ashish Thusoo presents the data scalability issues at Facebook and the data architecture evolution from EDW to Hadoop to Puma.
Kumar Palaniapan and Scott Fleming present how NetApp deals with big data using Hadoop, HBase, Flume, and Solr, collecting and analyzing TBs of log data with Think Big Analytics.
Jake Luciani introduces Brisk, a Hadoop and Hive distribution using Cassandra for core services and storage, presenting the benefits of running Hadoop in a peer-to-peer masterless architecture.
Jeff Lindsay discusses creating distributed and concurrent systems using ZeroMQ – a lightweight message queue-, and gevent – a coroutine-based networking library.
Dmitriy Setrakyan introduces GridGain, comparing it and outlining the cases where it is a better fit than Hadoop, accompanied by a live demo showing how to set up a GridGain job.