medium datasets under source control
If you're asking whether it would be efficient to put your datasets under version control, based on your description of the data, I believe the answer is yes.  Both Mercurial and Git are very good at handling thousands of text files. Mercurial might be a better choice for you, since it is written in python and is easier to learn than Git. (As far as I know, there is no good reason to adopt Subversion for a new project now that better tools are available.)
If you're asking whether there's a way to speed up your application's writes by borrowing code from a version control system, I think it would be a lot easier to make your application modify existing files in place. (Maybe that's what you're doing already?  It's not clear from what you wrote.)
This is more of a general question about how feasible is it to store data sets under source control.
I have 20 000 csv files with number data that I update every day. The overall size of the directory is 100Mbytes or so, that are stored on a local disk on ext4 partition.
Each day changes should be diffs of about 1kbyte.
I may have to issue corrections on the data, so I am considering versioning the whole directory= 1 toplevel contains 10 level1 dirs, each contain 10 level2 dirs, each containing 200 csv files.
The data is written to files by python processes( pandas frames ).
The question is about performance of writes where the deltas are small like this compared to the entire data.
svn and git come to mind, and they would have python modules to use them.
What works best?
Other solutions are I am sure possible but I would stick to keeping data is files as is...