Pat Patterson and Ted Malaska talk about current and emerging data processing technologies, and the various ways of achieving "at least once" and "exactly once" timely data processing.
Alan Ngai and Premal Shah discuss best practices on monitoring distributed real-time data processing frameworks and how DevOps can gain control and visibility over these data pipelines.
Gian Merlino discusses stream processors and a common use case - keeping databases up to date-, the challenges they present, with examples from Kafka, Storm, Samza, Druid, and others.
Christopher Meiklejohn talks through a history of chain replication, starting with the original work from 2004 by van Renesse and Schneider up to new and unique designs of chain replication.
Anil Madhavapeddy introduces the Irmin library by means of a functional queue, shows how the Git mirroring works, and then demonstrates some more complex applications.
Crista Lopes demos writing the same program using multiple styles, showcasing the richness of human computational thought and the need to avoid being stuck with one or two styles for life.
Vaclav Petricek discusses how to train models, architect and build a scalable system powered by Storm, Hadoop, Spark, Spring Boot and Vowpal Wabbit that meets SLAs measured in tens of milliseconds.
Tal Weiss explores five crucial Java techniques for distributed debugging and some of the pitfalls that make bug resolution much harder, and can even lead to downtime.
Dan Woods discusses the approach to developing a scalable enterprise architecture, and demonstrates implementations based on the variety of technologies available from the Groovy ecosystem.
Eugene Dvorkin provides an introduction to Storm framework, explains how to build real-time applications on top of Storm with Groovy, how to process data from Twitter in real-time, etc.
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.
Jamie Allen describes three patterns using Akka actors: handling a lack of guaranteed delivery, distributing tasks to worker actors and implementing distributed workers in an Akka cluster.