Eva Andreasson presents typical categories of problems that are commonly solved using Hadoop and also some concrete examples in each category.
Sean Owen provides examples of operational analytics projects, presenting a reference architecture and algorithm design choices for a successful implementation based on his experience Oryx/Cloudera.
Jonas Bonér, Francesco Cesarini discuss the evolution of distributed concurrent thinking along with the problems it has to solve and the toolchains created along the way.
Heather Miller presents attempts at better supporting distributed programming in Scala, including a new fast pickling framework, as well as Spores - composable pieces of mobile functional behaviour.
Josh Wills discusses using Hadoop technologies to build real-time data analysis models with a focus on strategies for data integration, large-scale machine learning, and experimentation.
Brenden Matthews describes the infrastructure built at Airbnb using Mesos in order to support Hadoop and Storm.
Matthias Broecheler discusses graph computing, introducing the Aurelius graph cluster enabling graph computing at scale by building on distributed systems like Cassandra, HBase, and Hadoop.
Rusty Sears introduces REEF along with examples of computational frameworks, including interactive sessions, iterative graph processing, bulk synchronous computations, Hive queries, and MapReduce.
Bikas Saha and Arun Murthy detail the design of Tez, highlighting some of its features and sharing some of the initial results obtained by Hive on Tez.
Ben Johnson discusses the Raft protocol and how it works. Raft is a consensus distributed protocol.
Steve Pember discusses creating Grails applications integrating message broker technologies, especially RabbitMQ, and applying SOA principles.
Sebastian Kanthak details how Spanner relies on GPS and atomic clocks to provide two of its innovative features: Lock-free strong reads and global snapshots consistent with external events.