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.
Michael Nygard shares essential Reliability Engineering techniques that can keep systems from falling apart, but the discipline has some limitations to be considered.
Blake Mizerany presents various ways that can lead to system failure in distributed systems and how to recover using Doozer, a highly available, consistent data store.