Akmal B. Chaudhri introduces Apache™ Hadoop® 2.0 and Yet Another Resource Negotiator (YARN).
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.
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.
Jeff Magnusson details some of Netflix' key services: Franklin, Sting and Lipstick.
Oleg Zhurakousky discusses architectural tradeoffs and alternative implementations of real-time high speed data ingest into Hadoop.
Uri Laserson reviews the different available Python frameworks for Hadoop, including a comparison of performance, ease of use/installation, differences in implementation, and other features.
Michael Kopp explains how to run performance code at scale with Hadoop and how to analyze and optimize Hadoop jobs.