requests VS Airflow

For many years now, Requests has been frozen. Being left in a vegetative state and not evolving, this blocked millions of developers from using more advanced features.

- Ubiquitous “requests” library used in most docs examples, no async support https://github.com/psf/requests

Explore here.

It's Lukasa (his name is Cory, there's Łukasz in PSF though, but that's a different person). Looking at him, he made significant contributions to the requests repo: https://github.com/psf/requests/graphs/contributors

> I'd love to see the look on some of these people's faces when they find out that tool/software/whatever they use is actually using libcurl under the hood.

Python dependencies (does not include curl)

https://devguide.python.org/getting-started/setup-building/i...

The "requests" module in Python (does not use curl)

https://github.com/psf/requests

This part can be found in the README of the GitHub repository.

Looking around for similar errors I found this issue where they recommended trying to use a newer version of the urllib3 library.

but I appreciate the fact that there is an issue about it, it's acknowledged and .. unfixable, it would now break too many things https://github.com/psf/requests/issues/2002

The requests package has been the golden standard for package structure for as long as I can remember.

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