In data-oriented programming (DOP), we model data as data and polymorphic behavior with pattern matching. This talk will introduce the concept of DOP and its four principles:

* model data immutably and transparently
* model the data, the whole data, and nothing but the data
* make illegal states unrepresentable
* separate operations from data

We'll also explore how to use pattern matching as a safe, powerful, and maintainable mechanism for ad-hoc polymorphism on such data that lets us define operations without overloading the types with functionality. The talk ends with a juxtaposition to OOP, so you not only learn how to employ DOP but also when (not).