versatile-data-kit VS Airflow

Free, Python+SQL ELT pipelines framework with orchestration functionality 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

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

Okay, I will explain what I am doing and how I see the "fun" in the project. I work with an open-source framework for data engineers. The community members are developers and people who use the tool - DEs. Indeed, I am facilitating a monthly community meeting for everyone to meet and discuss important topics, but that's the only part that takes their direct time, and it's totally voluntary, so DEs usually don't join, but I'm glad that the developers are joining and participating. What I was having in mind is more of a design and promotion question. I have a vision for open source projects to have a feel of friendliness, and openness (fun) which I communicate through design and visuals that are part of the repo and information we share about the project. And, as I don't find long texts engaging, because I literally can't focus when I see a long description of, say, a GitHub repo, I have an internal struggle against very detailed descriptions. That said, I am having an internal wish to transform the project into something more like this: https://github.com/mage-ai/mage-ai Instead of this: https://github.com/vmware/versatile-data-kit But I'm questioning myself, and thinking that maybe it is better suited for DEs as it is.

Opensource, good for basic SQL and/or Python skills, extensible and provides support in setup/adoption of the framework. https://github.com/vmware/versatile-data-kit I'm the community manager for this project, I built my first full ELT pipeline (tracking GitHub stats) with no previous experience on my first month totally by myself. It's covering the full data journey. Oh, and it has Airflow integration, with that you can have a dashboard to see your jobs, dependencies but has better/more intuitive scheduling.

In order to simplify steps 1-5 I can bring another framework to your attention - Versatile Data Kit (entirely open-source) which allows you to create data jobs (being it ingestion, transformation, publishing) with SQL/ Python, which runs on any cloud and is also multi-tenant.

I believe that you would not need to build the docker image yourself. There are data engineering frameworks which allow you to build your data jobs yourself and take care of the containerisation of your pipeline. You can have a look at this ingest from rest API example. They would also allow you to schedule your data job using cron, while data job itself can contain SQL & Python.

I wouldn't stay there if they absolutely disagree with changing things, it would drain my energy and I'd just get sad and depressed, on the other hand, if you decide to go for it and try to untangle this mess, I think it would contribute to the confidence, but take some real patience and persistence. I'm a real automation geek, everything that can be automated should be. Maybe if you wish for advice, I would check out this open-source DataOps / automation tool here: https://github.com/vmware/versatile-data-kit maybe it helps, maybe not, whatever you do, good luck!

I would recommend taking a look at Versatile Data Kit . It is an open-source tool that covers the full end-to-end cycle of data engineering with data ops practices embedded - from ingesting data from a source system, transformations (including implementation of some design patterns like Kimbal) and publishing data (for reports, apps) .

Airflow is the most widely used and well-known tool for orchestrating data workflows. It allows for efficient pipeline construction, scheduling, and monitoring.

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

Apache Airflow:

Looks interesting as a light-weight alternative to https://www.prefect.io/ (which itself is a lighter-weight / more modern alternative to https://airflow.apache.org/ ).

You can actually setup and delete new Redshift clusters using Apache Airflow. We can see in the example_dags of a DAG that does a complete setup and delete of a Redshift cluster. There are a few things to think about however.

A few years ago, I opened a GitHub issue with Microsoft telling them that I think the .NET ecosystem needs its own equivalent of Apache Airflow or Prefect. Fast forward 'til now, and I still don't think we have anything close to these frameworks.

Check out taskinstance.py in the Airflow project, it's a well targeted file, it has only one main class TaskInstance and a few small supporting classes and functions. It is ~3000 lines long: https://github.com/apache/airflow/blob/main/airflow/models/taskinstance.py