InfoQ Homepage Big Data Content on InfoQ
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APIs in Government
Gray Brooks discusses the efforts around creating APIs for accessing the vast amounts of data administered by the US Government.
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Expert Panel - The Relationship Between Big Data and the Semantic Web
Paul Buhler, Steve Hamby, Johan Kumps, Art Ligthart, Markus Zirn, and Clemens Utschig discuss the relationships between Big Data and established Semantic Web technologies.
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Introduction to Spring Data
Mark Pollack provides a guided tour plus demos of the Spring Data feature set.
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Making Hadoop Real Time with Scala & GridGain
Nikita Ivanov shows adding real-time capabilities to Hadoop through a demo application streaming word counting on a 2-nodes cluster.
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Apache Cassandra Anti Patterns
Matthew Dennis covers the most common mistakes made with Cassandra that he has noticed being made both in deployment and code.
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How to Build Big Data Pipelines for Hadoop Using OSS
Costin Leau discusses Big Data, current available tools for dealing with it, and how Spring can be used to create Big Data pipelines.
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F# Big Data Scripting
Matthew Moloney shares some of the F# tools built at Microsoft Research for dealing with Big Data.
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The Evolving Panorama of 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.
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Scaling Scalability: Evolving Twitter Analytics
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.
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Lean Data Architecture: Minimize Investment, Maximize Value
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.
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Storm: Distributed and Fault-Tolerant Real-time Computation
Nathan Marz introduces Twitter Storm, outlining its architecture and use cases, and takes a look at future features to be made available.
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Extending the Enterprise Data Warehouse with Hadoop
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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