How do I make large datasets load quickly in Python?
Write a script that does the selects, the object-relational conversions, then pickles the data to a local file.
Your development script will start by unpickling the data and proceeding.
If the data is significantly smaller than physical RAM, you can memory map a file shared between two processes, and write the pickled data to memory.
I do data mining research and often have Python scripts that load large datasets from SQLite databases, CSV files, pickle files, etc. In the development process, my scripts often need to be changed and I find myself waiting 20 to 30 seconds waiting for data to load. 
Loading data streams (e.g. from a SQLite database) sometimes works, but not in all situations -- if I need to go back into a dataset often, I'd rather pay the upfront time cost of loading the data.
My best solution so far is subsampling the data until I'm happy with my final script. Does anyone have a better solution/design practice?
My "ideal" solution would involve using the Python debugger (pdb) cleverly so that the data remains loaded in memory, I can edit my script, and then resume from a given point.