Ben Christensen discusses the mental shift from imperative to declarative programming, working with blocking IO such as JDBC and RPC, service composition, debugging and unit testing.
Sandy Ryza aims to give a feel for what it is like to approach financial modeling with modern big data tools, using the Monte Carlo method for a a basic VaR calculation with Spark.
Howard Chu covers highlights of the LMDB design and discusses some of the internal improvements in slapd due to LMDB, as well as the impact of LMDB on other projects.
Chris Matts discusses how to manage product mastery, how do we decide whether to use analysis or product management techniques, and what does an end-to-end process looks like.
Tony Quinlan introduces the SenseMaker® method from preparing the ground through gathering experiences and qualitative material to analysis and action planning.
Piotr Kołaczkowski discusses how they integrated Spark with Cassandra, how it was done, how it works in practice and why it is better than using a Hadoop intermediate layer.
The authors present an approach for automatic translation of sequential, imperative code into a parallel MapReduce framework using Mold, translating Java code to run on Apache Spark.
Soumith Chintala introduces deep learning, what it is, why it has become popular, and how it can be fitted into existing machine learning solutions.
The authors introduce Cybertron, a new tool for reducing I/O operations in data-parallel programs through a constraint-based encoding.
Andrew Kennedy talks about the reasons for creating a Docker cloud and how Clocker was born.
Colin Mower discusses the challenges met using together Cloud, Big Data, Mobile and Security and how these can work together to achieve business value.
Kristoffer Dyrkorn presents the experiences gained by the Norwegian Public Roads Administration in building a new infrastructure for road traffic measurements.