Peter Bourgon presents some of the idioms, design patterns, and practices that have proven themselves developing successful, scalable, and sustainable code using Go.
Neha Narkhede explains how Apache Kafka was designed to support capturing and processing distributed data streams by building up the basic primitives needed for a stream processing system.
Manuel Fahndrich describes how they tackled one particular resource allocation aspect of Google Cloud Dataflow pipelines - horizontal scaling of worker pools as a function of pipeline input rate.
Ben Hall shares his experience working with Docket for development, testing and deployment into production, discussing scalability, resource management, security and other related issues.
Ian Cooper looks at Service Discovery and Clustering approaches and tools, and shows .NET developers how to work with tools such as Serf, Consul, and Zookeeper.
Dean Leffingwell describes the values, principles and practices of the Scaled Agile Framework, how it is delivering faster time-to-market, more engagement, higher quality, and increased productivity.
Mark Lines keynotes on the Disciplined Agile Delivery (DAD) framework, scaling an Agile strategy, and practices for successfully scaling Agile.
Mike Breeze and Ma Qiang share the story of a distributed team and its Agile transformation, placing individuals and interactions over processes and tools and avoiding the dark side of Agile.
John Blum and Luke Shannon introduce Pivotal GemFire along with the open source offering, Apache Geode. They demonstrate how to effectively build highly scalable applications with GemFire/Apache.
Matt Ranney covers the evolution of Uber's architecture and some of the systems they built to handle the current scaling challenges.
Marius Bogoevici demoes how to unleash the power of Kafka with Spring XD, by building a highly scalable data pipeline with RxJava and Kafka, using Spring XD as a platform.
S Aerni, S Ramanujam and J Vawdrey present approaches and open source tools for wrangling and modeling massive datasets, scaling Java applications for NLP on MPP through PL/Java and much more.