Explicit data size limits (something on the order of 1M rows at time of writing) are only one part of the problem in trying to use Excel with Big Data (TM). Generally, if you've got millions of rows, you're working with some sort of data export from another tool (e.g. different from some financial data you're using to model your companies finances).

And with millions of rows of data pouring out of some other tool, usually you're trying to define a repeatable process to clean/munge/transform that data into something more useful to you/your team/your management.

Within Excel, there are ways of accomplishing the "define repeatable task" goal - but my personal experience working with VBA (and talking to VBA users across the spectrum) is that it's a horrible language that is absolutely no fun at all to write. Good luck using a nice library to do anything with it, really.

I'm a co-founder of Mito [1], where we're taking a bit of a different angle. Rather than bringing big data into Excel, we're bringing an Excel ethos to where you might work with your big data otherwise. Mito is a spreadsheet interface that lives inside of a Jupyter notebook; you can write spreadsheet formulas, merge datasets, explore summary stats, all from within this spreadsheet. While you edit the spreadsheet, it generates valid Python code for you.

Our current users mostly fit the bill of "previous Excel junkies who started teaching themself Python but still have a lot to learn, so use Mito to augment/speed up their workflow."

Questions / comments / hard-hitting HN feedback greatly appreciated!

[1] https://trymito.io/launch