USAGE:

Install cookiecutter if you don't have it.

pip install cookiecutter

To start a new project run:

cookiecutter https://github.com/aguiarandre/etl-pipelines

for unix users or

cookiecutter.exe https://github.com/aguiarandre/etl-pipelines

for windows users.

This will create a file organization in the following structure:

Project Organization

├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── intermediate   <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
└── src                <- Source code for use in this project.
    ├── __init__.py    <- Makes src a Python module
    ├── client.py      <- Any external connection (via API for example) should be written here    
    ├── params.py      <- All parameters of the execution
    ├── pipeline.py    <- The ETL (extract-transform-load) pipeline itself containing the sequence of nodes
    │
    └── nodes          <- Scripts to containing each step of the ETL process.
         ├── data_preparation.py
         ├── data_gathering.py
         ├── data_transform.py
         ├── data_sotrage.py
         └── data_visualization.py

---

General Structure

The general idea is to centralize all steps of the pipeline in the nodes directory (submodule), the parameters in the params.py file, the connection in the client.py file and the pipeline itself on the pipeline.py file. Always specify (in params.py) files to be downloaded, uploaded or cached in the data folder.

Documentation

The initial documentation is also already updated. One can create the documentation by entering docs and typing:

./make.bat for windows users and ./make for unix users.

Also, to run the documentation as is, you'll have to install a requirement. To do that, just type

pip install -r requirements.txt