versatile-data-kit VS Airflow

Here's the project: https://github.com/vmware/versatile-data-kit

I don't really know if this would work for you because the UI is not functional yet, but a very simple REST API ingestion example here, there's one for csv too https://github.com/vmware/versatile-data-kit/wiki/Ingesting-data-from-REST-API-into-Database I can't imagine a simpler way unless it's really drag and drop.

I work at VMware and we use one tool for the whole ELT, it was made internally as there was no good alternative at the time and now we opensourced it, here it is: https://github.com/vmware/versatile-data-kit

With my team we created csv ingestion plugin described here, maybe you want to try it out: https://github.com/vmware/versatile-data-kit/wiki/Ingesting-local-CSV-file-into-Database

We use Versatile Data Kit for batch data job orchestration (https://github.com/vmware/versatile-data-kit)

"suggestions on how to reduce the time spent on initially generating and adjusting the code" is using some tools that automate ELT. Here's one open-source tool I'm working on with my team: https://github.com/vmware/versatile-data-kit

here's the repo: https://github.com/vmware/versatile-data-kit

If you wish to contribute, projects usually have good first issues: https://github.com/vmware/versatile-data-kit/labels/good%20first%20issue If you wish to learn, check out examples: https://github.com/vmware/versatile-data-kit/tree/main/examples

Have you heard about versatile data kit (https://github.com/vmware/versatile-data-kit)? I think it meets your needs perfectly:

Versatile Data Kit is a framework to bBuild, run and manage your data pipelines with Python or SQL on any cloud https://github.com/vmware/versatile-data-kit here's a list of good first issues: https://github.com/vmware/versatile-data-kit/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22 Join our slack channel to connect with our team: https://cloud-native.slack.com/archives/C033PSLKCPR

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