Uri Laserson reviews the different available Python frameworks for Hadoop, including a comparison of performance, ease of use/installation, differences in implementation, and other features.
Rebecca Parsons reviews some of the changes in how data is used and analyzed, looking at how data is used to track violence, and attempts to predict famine and other crises before they happen.
Karim Chine introduces Elastic-R, demonstrating some of its applications in bioinformatics and finance.
Andrew Clegg overviews methods and provides use cases for performing data sets operations like membership testing, distinct counts, and nearest-neighbour finding more efficiently.
Vaclav Petricek digs some of the romantic interactions nuggets hidden in eHarmony's large collection of human relationships.
Claudia Perlich keynotes on M6D’s approach to Big Data, using data granularity to build predictive models used for user targeting, bid optimization and fraud detection.
Ian Robinson discusses the complexity of highly connected data and how graph databases can help, illustrating the talk with practical examples implemented using Neo4j.
Rebecca Parsons proposes taking a different look at data, using different approaches and tools, then looks at some of the ways social data is used these days.
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.