Datapunt generic ETL command line scripts and functions for shell scripting in Docker.
At the City of Amsterdam we deal with many different types of structured and unstructered data. Much of the data is not of high quality and are missing proper semantics to do proper analytics with.
This repository combines generic command line functions to create extract, transform and load steps we can then use for creating a reproducable data for analytics and usage in dashboards and maps.
For more information about the how we use these functions in our workflow, read the data-pipeline guide.
How to use
Full documentation can be found here: amsterdam.github.io/data-processing
Quickstart: To use a function in python you can use:
from datapunt_processing.extract import download_from_catalog or from datapunt_processing.helpers.connections import objectstore_connection
To use the functions directly from the command line in your virtual environment or docker shell you can use it like this:
To see the list of command line functions see the modules below or directly in setup.py
To get the functions up and running:
pip install datapunt-processing
To develop the functions locally use these steps:
Clone the repository:
git clone https://github.com/Amsterdam/data-processing.git cd data-processing
Create Virtual environment in Windows
# Create and activate a virtual environment, for example with: python -m venv --copies --prompt data-processing .venv .venv\Scripts\activate
Create Virtual environment in OSX
virtualenv --python=$(which python3) venv source venv/bin/activate
Install the data-processing modules in editable mode
pip install -e .
4. A database is required for the transform and load functions. You can setup your postgres database credentials in the config.ini file to apply to the functions.
If want to use Docker, you can start a database server for your project in a new terminal. The name, port and login of the database can be changed in the docker-compose.yml. Also change them in the config.ini file which will be used by the functions to connect to that database.
docker-compose up -d database
Some of the examples are in the form of runnable Jupyter notebooks. Copies of these with all the images and output included are hosted at Anaconda Cloud. To run these notebooks on your own system, start up a Jupyter notebook server:
To install jupyter:
pip install -e .\[dev\] jupyter notebook --NotebookApp.iopub_data_rate_limit=100000000
How to Contribute
If you want to contribute please follow the contribute guidelines
Fork this repository to your local github account to add add and test new functions.
git clone https://github.com/Amsterdam/data-processing.git
Install the docs,test,dev packages using this command:
pip install -e .[docs,test,dev] or when using zsh pip install -e .\[docs,test,dev\]
1. Add function
We try to use command line functions as much as possible to ensure we create functions to work easily with different environments and to force yourself creating more generic functions with input variables.
side note: not all functions are suitable for CL. Machine learning preprocessing steps or general API calls for instance, (that often require parameters in the form of dicts or lists) as input are not suitable and can be used as stand-alone scripts.
2. Add tests
Add test to the test folder and run:
python setup.py test
to test if no other functions are breaking. Correct those issues as well if needed.
3. Add documentation
Create a awesome_module.rst file with Sphinx Argparse extension fields to generate the description and argument fields by reusing an existing rst file. The helpers docs will generate automatically, so you can skip this step if it is placed in the helper function.
Add the rst filename to the list in modules.rst to be found on the main page.
Regenerate the documentation to test the docs output using this command line function:
sphinx/make docs and test if the readme is not broken: open docs/index.html
4. Add a Pull Request
Make a PR to add the add your awesome function to our processing code to be reused by many other developpers and data analists.
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