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.
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.
Manvir Singh Grewal and Brandon Byars propose a business intelligence workflow along with Lean principles and practices for implementing a data warehouse and reporting capability.
Nathan Marz introduces Twitter Storm, outlining its architecture and use cases, and takes a look at future features to be made available.
Rob Lancaster explains the steps made by Orbitz in order to bridge the gap between their data in the data warehouse and the data in Hadoop.
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