Sean Owen introduces Spark, Scala and random decision forests, and demonstrates the process of analyzing a real-world data set with them.
Dave McCrory talks about what is Data Gravity, how it affects performance and portability and why these effects are amplified when there are larger volumes of data.
Amy Phillips explains how the core principles can be used to drive process change and how their team removed many of the delays and frustrations from their release process.
Rachel Laycock focuses on the architecture of an application, addressing patterns such as microservices and evolutionary architecture, which can speed up delivery.
Brian Cavalier shows how Differential Synchronization can be used with JSON Patch to synchronize application data between clients and servers over HTTP Patch, WebSocket, and STOMP.
Omer Shapira introduces HTTP/2 (and SPDY), exploring the impact the protocol has on application design, and telling the story of LinkedIn adopting SPDY on its network infrastructure.
Roy Rapoport shares some of the lessons Netflix learned building a monitoring system, the challenges, pitfalls and opportunities encountered along the way.
Rebecca Parsons explores the relationship between evolutionary architecture, continuous delivery and microservices, focusing on how they support each other in the creation of complex systems.
Francesco Cesarini illustrates how the Erlang way of thinking about problems leads to scalable and fault-tolerant designs, describing 3 ways of clustering Erlang nodes within the server side domain.
John Wilkes shares lessons learned managing clusters at the scale of Google.
Anna Shipman explains how the GOV.UK team implemented the DevOps culture – the people, the process, and the technical details of what tools they use and how they are integrated.
Thore Thomassen shares from experience how to combine structured data in a DWH with unstructured data in NoSQL, and using parallel data warehouse appliances to boost the analytical capabilities.