Django + Postgres + Large Time Series
You might also consider using the PostGIS postgres extension which includes support for raster data types (basically large grids of numbers) and has many features to make use of them.
However, do not use the ORM in this case, you will want to do SQL directly on the server. The ORM will add a huge amount of overhead for large numerical datasets. It's also not very adapted to handling large matrices within python itself, for that you need numpy.
I am scoping out a project with large, mostly-uncompressible time series data, and wondering if Django + Postgres with raw SQL is the right call.
I have time series data that is ~2K objects/hour, every hour.  This is about 2 million rows per year I store, and I would like to 1) be able to slice off data for analysis through a connection, 2) be able to do elementary overview work on the web, served by Django.  I think the best idea is to use Django for the objects themselves, but drop to raw SQL to deal with the large time series data associated.  I see this as a hybrid approach; that might be a red flag, but using the full ORM for a long series of data samples feels like overkill.  Is there a better way?