Jim Scott keynotes on the history of Hadoop, the difficulties that this technology has gone through, exploring the reasons why enterprises need to evaluate their targets and prepare for the future.
Randy Shoup describes KIXEYE's analytics infrastructure from Kafka queues through Hadoop 2 to Hive and Redshift, built for flexibility, experimentation, iteration, testability, and reliability.
Marcel Kornacker presents a case study of an EDW built on Impala running on 45 nodes, reducing processing time from hours to seconds and consolidating multiple data sets into one single view.
In this solutions track talk, sponsored by Cloudera, Eva Andreasson discusses how search and Hadoop can help with some of the industry's biggest challenges. She introduces the data hub concept.
In this solutions track talk, sponsored by MongoDB, Matt Asay discusses the differences between some of the NoSQL and SQL databases and when Hadoop makes sense to be used with a NoSQL solution.
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.