didact-engine VS Airflow

I think part of that issue today are monorepos. They are super convenient and I may end up using them for my open source .NET job orchestrator, Didact, but they are overwhelming to look at a lot of times - and I’ve been in the field for yours. I used to get so frustrated with web dev in particular a few years ago, never understood what the heck I was looking at.

In particular, I’m building an open source .NET/C# job orchestrator called Didact. It’s taken a lot of inspiration from Apache Airflow and Prefect over in the Python world. It isn’t finished yet, hoping to have a rough v1 ready after the new year.

I’m building an open source .NET/C# job orchestration platform called Didact. I have a landing page for the site, and right now each piece of my architecture is in its own, separate repo on GitHub.

I'm building an open source .NET job orchestration platform called Didact. It's not so much a library like the options above, but more like a complete, independent platform to run on its own. I'm including a REST API/execution engine, a modern-looking web dashboard UI (similar to Hangfire), and a few other things.

You could make something open source if you want. Nice way to build your resume and establish a reputation for yourself. Who knows, you could even try to live off of it one day. That’s what I’m attempting with my new open source platform, Didact, which is a brother of Apache Airflow for .NET/C#. You can find some cool stories about this on IndieHackers.com.

I’m building a job orchestrator for dotnet/csharp called Didact (website is almost done, but here is my main GitHub repo: https://github.com/DidactHQ/didact-engine).

I’ve got a few GitHub repos for it, here is the main one: https://github.com/DidactHQ/didact-engine

Didact Engine

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