Michael Hausenblas introduces containers, microservices and dealing with security, monitoring and troubleshooting using Apache Mesos/Marathon and Kubernetes.
Anthony McCulley describes The Home Depot’s first year with Cloud Foundry, adopting the platform, scaling to hundreds of developers across multiple data centers, and mistakes made along the way.
Trisha Gee highlights the performance benefits of using Java 8, pros and cons, identifying code that makes sense to refactor with lambdas and streams, and what changes provide the most benefit.
Luke Kosewski describes Flow, how it adds value to a microservice architecture, what preconditions must be met for such a recovery mechanism to succeed, and tells the story of a 2015 Q4 outage.
Alex Rasmussen examines some lessons learned while building record-setting sorting systems at UC San Diego.
Chris Seaton introduces Graal, Oracle Labs' new JIT compiler written in Java, enabling new research into optimizations, and Truffle, a framework for implementing languages that uses Graal.
David Greenberg discusses how Two Sigma was able to scale up their research to harness tens of thousands of CPUs and the challenges faced.
Matt Ranney talks about Uber’s growth and how they’ve embraced microservices. This has led to an explosion of new services, crossing over 1,000 production services in early March 2016.
Tony Grout and Chris Matts share stories from their several year multi-company journey towards scaled agile, showing how to look at Agile from an organizational perspective and not through tools.
Matt Warren takes a look at how to measure, what to measure and how get the best performance from .NET code, considering examples from the Roslyn codebase and StackOverflow (the product).
Peter Bourgon and Matthias Radestock explain the theory behind Weave Mesh, some of the important key features, and demonstrate some exciting use cases, like distributed caching and state replication.
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.