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.
Sean Cribbs discusses Convergent Replicated Data Types, data structures that tolerate eventual consistency.
Ian Plosker explains why a data model needs to follow the query patterns when using a NoSQL storage solution.
Rich Hickey discusses the complexity introduced by a database into a system, and a way to deal with it by using Datomic. He also discusses immutability, epochal time, and persistent data structures.
Stuart Sierra discusses using a data-oriented programming approach in order to create programs that are easier to write and test. The session is accompanied with Clojure code samples.