Chris Mattmann envisions data science by integrating science software into rapid data production systems using cloud computing and open source software.
Igor Terzic presents several cases where Ember Data is used in production, and outlines some of the features that are intended to be included in the future.
Paul Chavard discusses advanced techniques for building large EmberJS applications with Ember Data.
Nick Kolegraff discusses common problems and architecture to support all the phases of data science and how to start a data science initiative, sharing lessons from Accenture, Best Buy, and Rackspace.
Sebastian Kanthak overviews Spanner, covering details of how Spanner relies on GPS and atomic clocks to provide two of its most innovative features: Lock-free strong (current) reads and global snapshots that are consistent with external events.
Dan Frank discusses stream data processing and introduces NSQ – Bitly’s open source queuing system – and other new technologies used for communication between streaming programs.
Alex Miller discusses Clojure’s approach to data, comparing it with OOP’s approach, and covering various related topics such as mutation, state vs. value, primitive and composite data.
Andrew Clegg overviews methods and provides use cases for performing data sets operations like membership testing, distinct counts, and nearest-neighbour finding more efficiently.
Scott Vokes presents some lesser-known data structures and shows how probability distributions and content-addressable storage can become tools to shape global system behavior.
The panelists discuss their approaches in using APIs and open standards and data in the education sector.
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.