Indrajit Roy presents HP Labs’ attempts at scaling R to efficiently perform distributed machine learning and graph processing on industrial-scale data sets.
Nick Kolegraff discusses common problems and architecture to support all the phases of data science and how to start a data science initiative, sharing lessons from Accenture, Best Buy, and Rackspace.
Paco Nathan reviews an example data analysis application written in Cascalog used for a recommender system based on City of Palo Alto Open Data.
Avi Bryant discusses how the laws of group theory provide a useful codification of the practical lessons of building efficient distributed and real-time aggregation systems.
Ken Collier discusses Agile Analytics, a combination of sophisticated analytics techniques, lean learning principles, agile delivery methods, and "big data" technologies.
Mike Nolet shares lessons learned scaling AppNexus and architectural details of their system processing 30TB/day: Hadoop, DNS built in GSLB and Keepalived, and real-time data streaming built in C.
Michael Hausenblas introduces Apache Drill, a distributed system for interactive analysis of large-scale datasets, including its architecture and typical use cases.
Jim Webber explains how to understand the forces and tensions within a graph structure and to apply graph theory in order to predict how the graph will evolve over time.
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.