cookiecutter-flask VS Airflow

This can help you

May you want to see this

No GUI but you can use https://github.com/cookiecutter-flask/cookiecutter-flask to generate a skeleton project. I personally don't use it as it's too opinionated for my liking, however, may find you can get up and running quickly.

you can use any cookiecutter for flask. https://github.com/cookiecutter-flask/cookiecutter-flask have a look

A quick Google search can reveal so much. As a programmer you should be very capable of finding it yourself (;

Well look, I would encourage you to, and it's easy to, start from a position of knowing that your code will run on a server. You could use cookiecutter-flask, or you could just find a heroku template for a Flask app - cloning it and deploying it becomes quite simple, and it is helpful (IMO) to know for a fact that everything you're doing is going to work fine on the server, and to grow accustomed to working in a "I'm going to push this feature to the server when it's finished" type of workflow.

I take it that you have never heard of cookiecutter or the recipe for starting a flask project?

Cookiecutter flask is a template for large projects, it’s always a great place to start and compare against https://github.com/cookiecutter-flask/cookiecutter-flask

Contributing to Apache Airflow's open-source project immersed me in collaborative coding. Experienced maintainers rigorously reviewed my contributions, providing constructive feedback. This ongoing dialogue refined the codebase and honed my understanding of best practices.

In week Two, I contributed to the Apache Airflow repository.

Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. The platform features a web-based user interface and a command-line interface for managing and triggering workflows.

Airflow is the most widely used and well-known tool for orchestrating data workflows. It allows for efficient pipeline construction, scheduling, and monitoring.

AIRFLOW This is more of a library in my opinion, but Airflow has become an essential tool for scheduling in my work. All our ML training pipelines are ordered and scheduled with Airflow and it works seamlessly. The dashboard provided is also fantastic!

I agree there are many options in this space. Two others to consider:

- https://airflow.apache.org/

- https://github.com/spotify/luigi

There are also many Kubernetes based options out there. For the specific use case you specified, you might even consider a plain old Makefile and incrond if you expect these all to run on a single host and be triggered by a new file showing up in a directory…

You can try and contribute some new connectors/operators for workflow managers like Airflow or Airbyte