Erlang is a programming language dedicated to building robust and scalable services. It has matured for many years and finally reached a point where it is one of the secret weapons used by many prominent companies to build large-scale systems. Mickaël walks you through computing history to understand the concepts behind Erlang and explains how they influenced the design of modern languages.
Computing isn't physics. But as we add scale to computing systems, we can begin to reason from simple principles to detailed understanding of how our systems must work. These constraints, in turn, shape our entire industry in predictable ways. Ted gives two examples: the deep reason big data is big right now and how messaging systems must work.
Developing a modern application oftern means composing it with several services, many of them third-party. While this offers many benefits, it also traps your data in silos, preventing a holistic view. Eliot presents the cross-service-join: a means of querying across multiple services from a central database.
Scaling up machine learning techniques to keep pace with "big data" is its own interesting engineering problem. Recently, Apache Spark has become a popular framework for large-scale ML. Sean introduces the intuition behind modern large-scale recommenders and highlights four ways ML algorithms are engineered to scale.