Sean T. Allen talks about creating repeatable tests using programmatic fault injection, message tracing, and auditing to create a trustworthy system which provides correct results.
Ian Fyfe discusses the different options for implementing speed-of-thought business analytics and machine learning tools directly on top of Hadoop.
Justin Smith discusses credential hygiene in distributed systems, covering topics such as key encrypting keys, hardware security modules, and promising advances in muti-party computation.
John Billings talks about winning over those skeptical about the benefits of microservices along with tips on caching, failure, interface changes, etc. for building a distributed system architecture.
Aysylu Greenberg revisits some features of modern distributed systems and shows three architectural patterns, their application, and reference papers that are relevant to today's distributed systems.
Caitie McCaffrey discusses the strategies for proving a correct system and less strenuous methods of testing, which can help increase our confidence that a system is doing the right thing.
Colin Humphreys talks about how the Cloud Foundry community deploys the distributed system powering the one of the most successful open source platforms, along with details on the tooling used.
Sean Cribbs discusses practical applications of academic research with a large scale distributed system, as well as membership and dissemination protocols and their application in practice.
Theo Schlossnagle talks about lessons learned in building an always-on distributed time-series database with aggressive quality of service guarantees, and techniques for dealing with bad machines.
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.
Brandon Philips describes how bringing containers, schedulers, and distributed systems together will create more reliable and greatly more trusted server infrastructures.
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.