InfoQ Homepage Distributed Systems Content on InfoQ
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Architecting a RESTful Cloud - The Key to Elasticity
Jason Bloomberg explains the architectural requirements for Cloud-based applications and how REST can be used to achieve elasticity in the cloud.
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Petabyte Scale Data at Facebook
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.
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Clojure + Datomic + Storm = Zolodeck
Amit Rathore describes the architecture of Zolodeck, a virtual relationship manager built on Clojure, Datomic, and Storm.
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Big Time: Introducing Hadoop on Azure
Yaniv Rodenski introduces Hadoop, then running Hadoop on Azure and the available tools and frameworks.
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Building Healthy Distributed Systems
Mark Phillips discusses 3 types of distributed systems and how they run them at Basho: Computer Systems, Communities, and Companies.
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Embracing Concurrency at Scale
Justin Sheehy discusses designing reliable distributed systems that can scale in order to deal with concurrency problems and the tradeoffs required by such systems.
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MapReduce and Its Discontents
Dean Wampler discusses the strengths and weaknesses of MapReduce, and the newer variants for big data processing: Pregel and Storm.
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Hadoop: Scalable Infrastructure for Big Data
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.
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Storm: Distributed and Fault-tolerant Real-time Computation
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.
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Big Data Architectures at Facebook
Ashish Thusoo presents the data scalability issues at Facebook and the data architecture evolution from EDW to Hadoop to Puma.
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NetApp Case Study
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.
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Hadoop and Cassandra, Sitting in a Tree ...
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.
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