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.
Shobana Radhakrishnan shares details about best practices adopted in implementing API integration with third party services, how to manage change and deal with failures.
Christophe Grand tells Clojure stories full of immutability, data over behavior, relational programming, declarativity, incrementalism, parallelism, collapsing abstractions, local state and more.
Steven Ihde and Karan Parikh discuss about tools and frameworks built in order to help LinkedIn's transition to microservices, including their URN resolution engine and the Rest.li API Hub.
Paul Osman discusses their experiences evolving 500px from a single, monolithic Ruby on Rails application to a series of composable microservices written in Ruby and Go.
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.
Adam Parker tells how they planned and ran a diary study, what they did during the 3 weeks of the study, how they analyzed the results, and what they learned by doing it.
Colin Burns takes a look at the creative forces shaping BBC’s approach to the future, examining how to create innovative experiences across different screens.
Thomas Kristensen describes the overall architecture of Composer, a system for composing musing, showing how to build a system that achieves responsiveness while still being flexible.
Gian Merlino presents the advantages, challenges, and best practices to deploying and maintaining lambda architectures in the real world, using the infrastructure at Metamarkets as a case study.
Dan Macklin explains why bet365 has adopted Erlang as a core development platform and goes through the highs and lows of managing change in one of the world's biggest on-line bookmakers.
Lin Qiao discusses the architecture of Gobblin, LinkedIn’s framework for addressing the need of high quality and high velocity data ingestion.