I really think this is where pure FP shines.<p>If you look at the architecture of something like Apache Beam, while you describe your computations in a language like Java or Python, you're really using a DSL for creating an immutable DAG that processes chunks of immutable data, persisiting each chunk (being loose with terms here) for durability, 'modifying' it in the immutable sense and then passing it on to the next edge and so on.<p>In a single standalone system, many argue that a purely immutable conceptual approach has pros and cons. In the Big Data world, I've never heard anyone argue for an imperative approach. Immutability is necessary, and as soon as you need immutability you want all the other goodies that facilitate it in a clean way like monads, lenses, and the like.