n8n VS Airflow

By developing many small modules that are meant to run within the generalised machinery. Something like this: https://github.com/n8n-io/n8n/tree/master/packages/nodes-bas...

Link to the Github repository: https://github.com/n8n-io/n8n

I believe you can achieve that with n8n. Used in past (and still running) for some data transformation and little more. Possibly similar case what are you describing.

https://n8n.io/

A startup, "DevOps Solutions" adopts Helm to streamline their Kubernetes deployments. You're a consultant tasked with creating a basic Helm Chart for n8n. It should be customizable for different environments using values.

n8n.io - a powerful workflow automation tool

N8N - Open Source Alternative to Zapier

N8N - anything that I would have used Zapier or IFTTT for I now use N8N. It is a bit harder to use but more powerful.

An integral part of an ML project is data acquisition and data transformation into the required format. This involves creating ETL (extract, transform, load) pipelines and running them periodically. Airflow is an open source platform that helps engineers create and manage complex data pipelines. Furthermore, the support for Python programming language makes it easy for ML teams to adopt Airflow.

Level 1 of MLOps is when you've put each lifecycle stage and their intefaces in an automated pipeline. The pipeline could be a python or bash script, or it could be a directed acyclic graph run by some orchestration framework like Airflow, dagster or one of the cloud-provider offerings. AI- or data-specific platforms like MLflow, ClearML and dvc also feature pipeline capabilities.

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…