If we could visualize the entirety of data within django-projects, we
would probably see that this 'data economy' is growing exponentially
year-over-year. However, I know of no guided way to actually get this
data into a project that's been converted to Django. There are two
methods I generally hear about when asking people how to move between
schemas: purely SQL solutions and one-shot scripted solutions (or a
mix). With talk of model-level validation, the first approach is
becoming increasingly invalid, but I wonder if we could include some
batteries for the second approach?

My proposal is a new django class which provides a mapping for how
this data should move from its legacy schema to a django project. I've
got a sort of proof-of-concept already working but it lacks the polish
of a refined community contribution. Moreover, with multi-database
support coming, I see this concept getting a shot in the arm,
especially in cases where the legacy db is a currently supported one.

I imagine the usage going something like:

1) User creates django project

2) User runs a 'startconversion' app which creates a stage folder for
holding an inspectdb of the legacy data, a default router for the
legacy data, and some other empty files.

3) User defines the classes which defines the map between the legacy
and new schema, and defines clean functions according to their needs,
'foreignkeys' to other conversion classes, etc.

4) User runs a command at the top branch of their schema (some distant
relation) and the command inspects these classes and runs them from
the ground up. As it does this measures are taken (such as use of
pagination) to avoid server CPU/memory thrashing, as well as model-
level measures such as OneToOne's being respect, etc.

-Steve

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