I've been reading about the star schema (or dimensional) database structure, which puts all measurements main in 'facts' table, and all context for those measurements in 'dimension' table linked to the facts table (I'm doing a horrible job explaining this), so that you can query any measurement by conditioning on the dimension.
When I read about this, it struck me as a perfect way to aggregate different csv files with different columns into one store... for example, stock, commodities, and options data in various files go be shoved into a star-schema db with a facts table containing the open/close/high/low/volume, with foreign-key links to dimensions such as time, company, exchange, and 'file pedigree' so that we know where each entry comes from. That would allow you to query for, say, first quarter chinese mining companies with a single, fairly simple sql statement.
However, all of the literature on this topic seems to be for large scale 'enterprise' deployments and not small data analysis or research jobs. Does anyone have any experience organizing their data this way? Is there are simpler way?