Ian Plosker shares a number of techniques for establishing the data query patterns from the outset of application development, designing a data model to fit those patterns.
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.
Steve Miner discusses tag literals and data readers, what’s new in Clojure 1.5, and EDN (Extensible Data Notation).
Stephane Dubois shares insight in Xignite’s road building a business model providing APIs for accessing financial data.
Ian Robinson discusses the complexity of highly connected data and how graph databases can help, illustrating the talk with practical examples implemented using Neo4j.
Gray Brooks discusses the efforts around creating APIs for accessing the vast amounts of data administered by the US Government.
David Rogers outlines how a highly-scalable RDF and SPARQL-based API was delivered, how a graph of highly-connected data can be managed effectively across a large organization, and their plans to open up access to the BBC's data from Bitesize learning resources, to the Radio 4 archive.
Paul Ingles explains how Clojure’s approach to immutable data has helped uSwitch to treat everything as data and build many tools that operate on the same data without contention.
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.
Dhruba Borthakur discusses the different types of data used by Facebook and how they are stored, including graph data, semi-OLTP data, immutable data for pictures, and Hadoop/Hive for analytics.
Scott Vokes presents several less known data structures and their advantages: skiplists, difference lists, rolling hashes, and jumpropes.