Agile Functional Data Pipeline in Haskell: A Case Study of Multicloud API Binding

This SkillsCast is currently only available to registered attendees of Haskell eXchange 2020

It will be freely available to all Skills Matter members once the Haskell eXchange 2020 early-access window expires on December 04, 2020.

In this case study, Michał discusses making a data pipeline for multicloud API bindings in Haskell for analysis and Python for scraping. He'll introduce a few rules settled on along the way that allow for blinding fast development of agile data analytics.

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.

View the slides from this talk here:

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