InfoQ Homepage Big Data Content on InfoQ
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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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Data, Be Like Water
Paul Sanford presents the transformations supported by data throughout its life cycle, and how that can be better done with Splunk, an engine for monitoring and analyzing machine-generated data.
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Machine Learning on Big Data for Personalized Internet Advertising
Michael Recce discusses how advertising works and what algorithms Quantcast uses to analyze large amounts of data in order to find out what people are interested in.
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Grid Gain vs. Hadoop. Why Elephants Can't Fly
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.
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Data Infrastructure @ LinkedIn
Sid Anand presents the architecture set in place at LinkedIn and the data infrastructure running Java and Scala apps on top of Oracle, Voldemort, DataBus and Kafka.
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Dynamo Is Not Just for Datastores
Susan Potter discusses Dynamo, Riak, distribution, consistency and fault tolerance, along with techniques and an example for building an application with riak_core.
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The Evolving Panorama of Data
Martin Fowler and Rebecca Parsons discuss how data has changed over the years, what is IT’s response to this change, and how data is used by organizations these days.
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Banking Case Study: Scaling with Low Latency using NewSQL
Jags Ramnarayan and Jim Bedenbaugh present the case of a bank who adopted SQLFire, covering the business requirements, the scalability issues, patterns used and the chosen solution.
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An Introduction to Doctor Who (and Neo4j)
Ian Robinson introduces Neo4J, a graph database, discussing how it can be used to store and work with data associated with Doctor Who.
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Distributed Data Analysis with Hadoop and R
Jonathan Seidman and Ramesh Venkataramaiah present how they run R on Hadoop in order to perform distributed analysis on large data sets, including some alternatives to their solution.
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