Development in Python according to 12 SaaS principles with the | Big Data Science

Development in Python according to 12 SaaS principles with the Python-dotenv library
ML modelers and data analysts don't always write code like professional programmers. To improve the quality of the code, use simple methodology for developing web applications or SaaS. It recommends:
• use of declarative formats for registration to establish the time and strength of new commitments joining the project;
• have a clean agreement with the underlying system, providing portability between environments;
• start deployment on modern cloud platforms, eliminating the need to administer servers and systems;
• reduce spread between origin and production, ensure continuous deployment for rapid agility;
• scale without major changes in tooling, architecture, or development methods.

To implement these SaaS ideas, it is proposed to build applications on 12 repositories:
1. One codebase is version controlled, many are deployed
2. Explicitly declare and isolate the dependency
3. Keep health in the environment
4. Treat supporting services as attached resources
5. Strictly separate the stages of assembly and launch
6. Use the application as one or more stateless processes
7. Export services via port binding
8. Modify parallelism by scaling with the process model.
9. Maximum reliability due to fast start-up and smooth shutdown
10. Portability and credibility of environments from development to production through tests
11. Log, view event stream logs
12. Performs administration/management tasks as one-time processes

To implement all this for a Python program open library Python-dotenv. It reads key-value pairs from the .env file and can consider them as environment variables. If the application meets the requirements of the environment, running it during development is not very practical, because the developer needs to set these environment variables themselves. By adding Python-dotenv to your application, you can simplify the development process. The library itself loads the settings from the .env file, while remaining configurable through the environment.
You can also load the configuration without environment changes, parse the configuration as a stream and .env files in IPython. The tool also has a CLI interface that allows you to manipulate the .env file without manually opening it.
https://github.com/theskumar/python-dotenv