make-booster VS Airflow

For a very different approach, check out make-booster:

https://github.com/david-a-wheeler/make-booster

Make-booster provides utility routines intended to greatly simplify data processing (particularly a data pipeline) using GNU make. It includes some mechanisms specifically to help Python, as well as general-purpose mechanisms that can be useful in any system. In particular, it helps reliably reproduce results, and it automatically determines what needs to run and runs only that (producing a significant speedup in most cases). Released as open source software.

https://github.com/david-a-wheeler/make-booster

I think a lot of hate on make is due to poor use. If your makefile is complex, refactor it. Auto-generate dependencies (it only takes a few lines in GNU make). And don't use recursive make, that way lies madness. I also think GNU make is the wiser tool; POSIX make lacks too much in many cases.

https://github.com/david-a-wheeler/make-booster

From its readme:

"This project (contained in this directory and below) provides utility routines intended to greatly simplify data processing (particularly a data pipeline) using GNU make. It includes some mechanisms specifically to help Python, as well as general-purpose mechanisms that can be useful in any system. In particular, it helps reliably reproduce results, and it automatically determines what needs to run and runs only that (producing a significant speedup in most cases)."

"For example, imagine that Python file BBB.py says include CC, and file CC.py reads from file F.txt (and CC.py declares its INPUTS= as described below). Now if you modify file F.txt or CC.py, any rule that runs BBB.py will automatically be re-run in the correct order when you use make, even if you didn't directly edit BBB.py."

This is NOT functionality directly provided by Python, and the overhead with >1000 files was 0.07seconds which we could live with :-).

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