Travis Reeder thinks performance, memory, concurrency, reliability, and deployment are key to exploring Go and its value in production. Travis describes how it’s worked for Iron.io.
Jordan Day introduces the Elixir language, its syntax and the semantics of an Elixir application, highlighting differences that make Elixir apps more reliable than those written in other languages.
Brian Shirai proposes using different interoperable languages throughout the life of a product, how to build reliable systems from less reliable components, along with examples from Rubinius 3.0.
Donald Belcham explains how to improve a system’s reliability by using appropriate code patterns.
Bart De Smet explains what it took to bring the concepts of Reactive Extensions (Rx) to the cloud to deal with latency, scale, reliability, and other concerns.
The authors discuss patterns and technologies needed to scale large enterprise mobile systems, covering handling network connectivity, data reliability and real-time communication.
Camille Fournier explains what projects ZooKeeper is useful for, the common challenges running it as a service and advice to consider when architecting a system using it.
Randy Shoup describes KIXEYE's analytics infrastructure from Kafka queues through Hadoop 2 to Hive and Redshift, built for flexibility, experimentation, iteration, testability, and reliability.
Robert Virding describes how Erlang was developed to solve the concurrency and reliability requirements of telecommunications, dealing with challenges that are similar with those of cloud computing.
Blake Dournaee covers the often forgotten back-end architecture for mobile apps which should expose cross-platform APIs to mitigate some of the effects of mobile O/S fragmentation.
Garrett Smith discusses building reliable systems starting with lessons from Erlang, then outlining a set of principles and the practices for applying them in languages such as Ruby, Python, and Java.
Stephen Burton discusses how the people, processes, collaboration and tools employed in Formula 1 can be used to manage performance and reliability and ultimately achieve success by DevOps.