In the 11th episode of "Clojure in product. Would you do it again?", Artem Barmin and Vadym Kostiuk speak with Jeremiah Via, Staff Software Engineer at The New York Times. Jeremiah describes how Clojure was introduced and adopted across the search stack at a major media organization, and why JVM interop and practical tooling made it the right choice for their data-processing workloads.
Such innovations and community contributions as Absinthe for GraphQL API development, Oban for background job processing, Flop for Ecto queries, and others demonstrate Elixir's evolution into a mature platform for building scalable, maintainable applications.
When you start a machine learning (ML) project and select the technology stack, you expect it to provide data immutability, great parallel programming features, and excellent data manipulation and encoding capabilities. That’s exactly what Clojure brings to the table. Although it’s not the most popular choice for ML projects, its functional paradigm and the entire Java ecosystem behind its back can make it a game-changer.
Unlike imperative programming, which relies on mutable data and side effects, FP focuses on writing functions that produce predictable outputs based solely on their inputs. The versatility and expressiveness of Clojure make it applicable in various domains, from web development to scientific computing, from financial services to artificial intelligence.