This project aims to show a couple of things;
- How DevOps can leverage tooling of your application;
- How to test and see coverage statistics;
- How to handle deployables;
- How to generate deployables;
- How to handle semantic versioning;
This project is built upon docker and docker-composer. The compose container adds a built in jupyter notebook listening on port 8888;
$ make setup # Runs the docker-compose up
$ make pip # Install all dependencies of pipenv
After you will be able to point your browser to http://localhost:8888 and you will be able to see the jupyter notebook;
$ make app # Runs the app.py command
This command will run, loads the database of 156; Running tests:
$ make test
$ make shell
Just fork it, develop and then provide me an PR.
- https://resources.github.com/downloads/development-workflows-data-scientists.pdf;
- https://cdn2.hubspot.net/hubfs/532045/DataOps-an-Agile-Methodology-for-Data-Driven-Organizations-White-Paper-DataScience.com.pdf;
- https://medium.com/airbnb-engineering/scaling-knowledge-at-airbnb-875d73eff091;
- https://cdn2.hubspot.net/hubfs/532045/oracle-ds-roadmap-worksheet.pdf;
- https://interact.f5.com/rs/653-SMC-783/images/F5%20State%20of%20App%20Delivery%20Report%202017.pdf;
- https://medium.com/faun/devops-without-devops-tools-3f1deb451b1c;
- https://medium.com/experience-valley/utilizando-o-elk-stack-em-um-app-rails-como-ferramenta-de-business-intelligence-5e6ebe029422;
- http://slides.com/jonhnathatrigueiro/docker#/;
Check it out the "Releases" Page. https://github.com/joepreludian/recrutatech_devops_ia/releases