The task at hand was pretty simple — we wanted to create a flexible and reusable library of classes that would make the task of data validation (over Spark DataFrames) a breeze. In this article, I will cover a couple of techniques/idioms used for data validation. In particular, I am using the null check (are the contents of a column ‘null’). In order to keep things simple, I will be assuming that the data to be validated has been loaded into a Spark DataFrame named “df.”
Click through for those techniques.