Dmitriy Ryaboy shares some of the lessons learned scaling Twitter’s analytics infrastructure: Data loves a schema, Make data sources discoverable, and Make costs visible.
Cliff Click discusses RAIN, H2O, JMM, Parallel Computation, Fork/Joins in the context of performing big data analysis on tons of commodity hardware.
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.
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.
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.
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.
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.
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.
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.
Hilary Mason presents the history of machine learning covering some of the most significant developments taking place over the last two decades, especially the fundamental math and algorithmic tools employed. She also exemplifies how machine learning is used by bit.ly to discover various statistical information about users.
Ashish Thusoo and Namit Jain explain how Facebook manages to deal with 12 TB of compressed new data everyday with Hive’s help. Hive is an open source data warehousing framework built on Hadoop, allowing developers to perform analysis against large datasets using SQL.