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.
Piotr Kołaczkowski discusses how they integrated Spark with Cassandra, how it was done, how it works in practice and why it is better than using a Hadoop intermediate layer.
Oliver Gierke summarizes the problems Spring Cloud tries to solve and introduces the individual modules through practical code examples.
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.
Reid Draper shows how real world distributed database work, communicate and are tested, trading RPC for messaging, unit-tests for QuickCheck, and micro-benchmarks for multi-week stress tests.
Eugene Mandel discusses challenges of conforming data sources and compares processing stacks: Hadoop+Redshift vs Spark, showing how the technology drives the way the problem is modeled.
Michael Minella uses Spring XD and Spring Batch to orchestrate the full lifecycle of Hadoop processing and uses Apache Mahout to provide the audience with the recommendation processing.
Matt Stine presents how combine Spring Boot, Spring Data, Spring Reactor, Spring XD, Hadoop and run them in the cloud.
Armon Dadgar presents Consul, a distributed control plane for the datacenter. Armon demonstrates how Consul can be used to build, configure, monitor, and orchestrate distributed systems.
Chris Beams shares his findings from over two years of research into bitcoin and related technologies.
Steve Pember discusses the tenants of the Reactive Pattern and the importance of moving away from Monolithic to Reactive architectures.
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.