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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.
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Michał got PhD in structural bioinformatics pipelines, and after two postdocs in top research facilities moved on to use his expertise of data analysis platforms in commerce. After a stint in a bank, and fintech startup, he founded his own company.