Dean Wampler argues that Spark/Scala is a better data processing engine than MapReduce/Java because tools inspired by mathematics, such as FP, are ideal tools for working with data.
Bob Kelly presents case studies on how Platfora uses Hadoop to do analytics for several of their customers.
Jayesh Thakrar shows what can be done with irb, how to exploit JRuby-Java integration, and demonstrates how the Shell can be used in Hadoop streaming to perform complex and large volume batch jobs.
Carlos Queiroz introduces the lambda architecture and showcases how it can be implemented with SpringXD, GemFireXD and Hadoop in a CDR(Call Detail Record) mining application.
The authors explain how the Pivotal team leveraged familiar SQL-based queries to analyze fine-grained cluster utilization using Spring XD.
Mohammad Quraishi presents implementing a Big Data initiative, detailing preparation, goal evaluation, convincing executives, and post implementation evaluation.
Ted Dunning discusses the different options for running SQL on Hadoop including pros and cons.
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.
Ryan Cromwell introduces Elixir, a , functional distributed meta programming language inspired by Ruby and compiling to Erlang VM, covering pattern matching, pipelines and tail-call recursion.
Anthony Molinaro discusses the challenges of troubleshooting distributed systems and using Mondemand to track down issues with various services in a distributed system.
Chas Emerick discusses some of the common issues appearing in distributed systems and ways to solve them.
Camille Fournier explains what projects ZooKeeper is useful for, the common challenges running it as a service and advice to consider when architecting a system using it.