Dean Wampler supports using Functional Programming and its core operations to process large amounts of data, explaining why Java’s dominance in Hadoop is harming Big Data’s progress.
Francine Bennett keynotes on using big data in the cloud.
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.
Jeni Tennison explains how to evaluate an organization's data assets as potential sources of open data, and how to deal with the thorny issues of derived and personal data.
Gray Brooks discusses the efforts around creating APIs for accessing the vast amounts of data administered by the US Government.
Paul Buhler, Steve Hamby, Johan Kumps, Art Ligthart, Markus Zirn, and Clemens Utschig discuss the relationships between Big Data and established Semantic Web technologies.
Mark Pollack provides a guided tour plus demos of the Spring Data feature set.
Nikita Ivanov shows adding real-time capabilities to Hadoop through a demo application streaming word counting on a 2-nodes cluster.
Matthew Dennis covers the most common mistakes made with Cassandra that he has noticed being made both in deployment and code.
Costin Leau discusses Big Data, current available tools for dealing with it, and how Spring can be used to create Big Data pipelines.
Matthew Moloney shares some of the F# tools built at Microsoft Research for dealing with Big Data.
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.