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.
Ken Kousen discusses combining various technologies: Groovy, Ratpack, MongoDB, Grails, REST.
Sean Owen introduces Spark, Scala and random decision forests, and demonstrates the process of analyzing a real-world data set with them.
Evelina Gabasova explains how to run a social network analysis on Twitter and how to use data science tools to find out more about followers.
Dave McCrory talks about what is Data Gravity, how it affects performance and portability and why these effects are amplified when there are larger volumes of data.
Emily Green is taking a look at how SoundCloud uses Cassandra. She describes a couple of Cassandra instances, from the point of view of the products and functionality they support.