[prostodata]

> pull everything to my PC and use pandas, to take advantage of its ability to build up results piece by piece.

Most difficulties in data processing arise from the need to process multiple tables. It is highly difficult in SQL but it can be also difficult in pandas especially for complex analytical queries. Indeed, in pandas you still have to use the same join and groupby operations as in SQL.

An alternative to SQL, join-groupby, and map-reduce is developed in the Prosto library:

- https://github.com/prostodata/prosto Functions matter! No join-groupby, No map-reduce.

It is a layer over pandas and its main distinguishing feature is that it relies on functions and function operations for data processing as opposed to using only sets and set operations in SQL and other set-oriented approaches