Please log in to watch this conference skillscast.
After exposing a background of generating API bindings for the multicloud services, we use this case study to present our rules of thumb for agile data analytics development.
Michał presents examples with Haskell code and shows how best practices of functional programming solve practical problems of data analytics case-by-case. All cases are naturally motivated and embedded in this case study, but are illustrated with a short Haskell code sample.
The material is aimed at intermediate and expert Haskellers that want to reuse our techniques for other data analytics pipelines, or beginners who want to quickly learn the best monad to use when analysing thousands and millions of records on the input.
YOU MAY ALSO LIKE:
Agile Functional Data Pipeline in Haskell: A Case Study of Multicloud API Binding
Michał J Gajda is a bioinformatician turned data scientist, turned banker, turned software startup founder. He loves to mix the best of science and programming methodology into tasty dishes of ultimate utility.