how will cookiecutter handle Database driven projects about cookiecutter-data-science HOT 4 CLOSED

I agree this approach has worked well. I think the .env workflow may need some explanation in the docs so I'll open a separate issue for that.

from cookiecutter-data-science.

Here's how we've done this so far, just as one data point:

• A script (or scripts) in src/data/ will do all of the ETL. Right now in the template there is an example stub called make_dataset.py but it could be anything - you could have multiple files for interfacing with various systems or even multiple directories if things start to get too busy in src/. As you mentioned, adding a folder for saved SQL queries might be a good way to keep things tidy.
• That script dumps its output somewhere in the data/ directory.
• Optional: rule in the Makefile specifying the target (output) and dependencies (input files, such as the .py and .sql/.hql files).
• The project secrets (including database credentials) live in a file only on your machine called .env in the top level directory, which is in .gitignored by default to keep it out of version control. If you look at the stub make_dataset.py, it uses a package called python-dotenv to load up all the entries in this file as environment variables, so they are accessible with os.environ.get or whatever is language-appropriate.

Here's an example .env:

DATABASE_URL=postgres://username:password@localhost:5432/dbname
AWS_ACCESS_KEY=myaccesskey
AWS_SECRET_ACCESS_KEY=mysecretkey
...

Meta discussion:

• One of the core "opinions" of the project is that all code lives in src/ and everything in the data/ directory is just data.
• Another opinion is that end users should be liberal in adding folders to suit their needs but the template should be conservative in making those choices permanent.

Thoughts?

from cookiecutter-data-science.

completely agree with that approach. I think that works quite well.

from cookiecutter-data-science.

See #18 for .env issue - closing this issue for now but still very open to receiving comments.

from cookiecutter-data-science.