Marius Eriksen explains Twitter's experiences with functional programming (with Scala) @ Twitter: where functional techniques worked and where not. Also: how the Scala language has scaled with Twitter
Dean Wampler argues that Spark/Scala is a better data processing engine than MapReduce/Java because tools inspired by mathematics, such as FP, are ideal tools for working with data.
Jan Machacek demos creating and using reactive APIs in Scala with Spray and Akka.
Glen Peterson uses the Expression Problem to compare refactoring in Java, Scala and Clojure, showing how traits minimize changes in Scala when an interface changes and how Clojure avoids some issues.
Sponsored by Goldman Sachs. Java 8 has Streams, Scala has parallel collections, and GS Collections has ParallelIterables. How well do they perform?
Jack Singleton discusses how to improve code readability and maintainability in Scala, and how to be productive with Options, Immutability, and the Collections library.
Lance Walton shares the experience of a small team building a trading platform in 6 weeks in Scala and Lift while fighting against an opposing organizational culture.
Josh Suereth discusses Scala: expressions, abstracting behaviors, FP & OOP, Futures & Promises, libraries with implicit classes and value classes, tracking lexical state with implicit values.
Adam Warski shows how to replace features of DI containers with plain Scala code using MacWire, and adding interceptors using macros.
Marc-Daniel Ortega shares code snippets showing how to implement some logic in a functional language inspired by “Functional Programming in Scala”, avoiding the OOP influence.
Stefan Chis demoes building a Lisp dialect in Scala, covering: parsing code, defining data types and functions, evaluating expressions, implementing higher order functions.
Adam Rosien introduces scalaz, how to use it to make code simpler and type safer, how it compensates for Scala issues, and how it encapsulates DI and data validation.