Erik Hinton discusses the successes and failures of making a cultural shift in the newsroom at NYT to accept Haskell and some of the projects Haskell has been used for.
Cliff Click introduces a coding style & API for in-memory analytics that handles datasets from 1K to 1TB without changing a line of code and clusters with TB of RAM and hundreds of CPUs.
Josh Wills discusses using Hadoop technologies to build real-time data analysis models with a focus on strategies for data integration, large-scale machine learning, and experimentation.
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, load balancer-free DNS architecture 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 explores analytic techniques for graph data, discussing innate properties of (social) graphs from fields like anthropology and sociology. By understanding the forces and tensions within the graph structure and applying some graph theory, we'll be able 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.