InfoQ Homepage Data Analysis Content on InfoQ
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Big Data, Small Computers
Cliff Click discusses RAIN, H2O, JMM, Parallel Computation, Fork/Joins in the context of performing big data analysis on tons of commodity hardware.
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View Server: Delivering Real-Time Analytics for Customer Service
Richard Tibbetts presents a three-tier architecture for real-time data staging analysis, storing the results and delivering them to clients as a service accessible through a variety of interfaces.
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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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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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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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NoSQL at Twitter
Kevin Weil presents how Twitter does data analysis using Scribe for logging, base analysis with Pig/Hadoop, and specialized data analysis with HBase, Cassandra, and FlockDB.
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Machine Learning: A Love Story
Hilary Mason presents the history of machine learning covering the most significant developments in the area, and showing how bit.ly uses it to discover various statistical information about users.
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Facebook’s Petabyte Scale Data Warehouse using Hive and Hadoop
Ashish Thusoo and Namit Jain explain how Facebook manages to deal with analysis of 12 TB of compressed new data everyday with Hive’s help, an open source data warehousing framework built on Hadoop.
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