woodpecker
Airflow
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woodpecker | Airflow | |
---|---|---|
54 | 169 | |
3,664 | 34,485 | |
4.1% | 2.1% | |
9.9 | 10.0 | |
4 days ago | about 8 hours ago | |
Go | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
woodpecker
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The worst thing about Jenkins is that it works
https://github.com/woodpecker-ci/woodpecker
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Examples of Woodpecker (CI/CD) pipelines for .NET
Is anyone using woodpecker? It's a self-hosted CI/CD server forked from Drone. Really good, and actively developed.
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regularly updating a docker image from source across several servers
Run your own container registry, build and host everything yourself, dont rely on others. Docker for example has a option for that but imo its very basic and limited. Harbor is more advanced but still not overly complicated. You could add build workers to that and automate your entire pipeline, but maybe for a single image thats overkill. But good to have those options in the future. Things to look at for example: Gitea (lighter) / Gitlab (more heavy), Drone.io, Woodpecker
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GitHub: “Human eyes” will never see the contents of your private repositories
> I wish it had some sort of CI like github actions or bitbucket pipeline
I use Gitea with Drone CI and it works pretty well: https://www.drone.io/
Some might also prefer the Woodpecker CI fork due to the license: https://woodpecker-ci.org/
I setup Drone as a part of my migration away from GitLab Omnibus and have no complaints so far: https://blog.kronis.dev/articles/goodbye-gitlab-hello-gitea-...
- Woodpecker CI: simple, extensible CI engine powered by Docker
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What self-hosted Git server ?
https://woodpecker-ci.org/ Open source clone of drone.io
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GitHub actions top alternatives
https://www.drone.io/ or the more open fork https://woodpecker-ci.org/
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Codeberg – Fast Open Source Alternative to GitHub
I’m trying to migrate of my personal repos from GitHub to Codeberg. The biggest problem is to find a replacement for GitHub Actions (the free offering is so generous), and my current solution for that is to self-host an instance of Woodpecker CI [1].
I’d like to see even more diversity in Git hosting beyond “let’s all migrate from X to Y”, and for that to happen, Forgejo (a soft fork of Gitea) has already began implementing federation [2].
[1]: http://woodpecker-ci.org/
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JSON vs XML
The open source version of drone is https://woodpecker-ci.org/
- Woodpecker
Airflow
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Building in Public: Leveraging Tublian's AI Copilot for My Open Source Contributions
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.
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Navigating Week Two: Insights and Experiences from My Tublian Internship Journey
In week Two, I contributed to the Apache Airflow repository.
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Airflow VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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Best ETL Tools And Why To Choose
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.
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Simplifying Data Transformation in Redshift: An Approach with DBT and Airflow
Airflow is the most widely used and well-known tool for orchestrating data workflows. It allows for efficient pipeline construction, scheduling, and monitoring.
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Share Your favorite python related software!
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!
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Ask HN: What is the correct way to deal with pipelines?
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…
- "Você veio protestar para ter acesso ao código fonte da urnas. O que é o código fonte?" "Não sei" 🤡
- Cómo construir tu propia data platform. From zero to hero.
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Is it impossible to contribute to open source as a data engineer?
You can try and contribute some new connectors/operators for workflow managers like Airflow or Airbyte
What are some alternatives?
drone - Gitness is an Open Source developer platform with Source Control management, Continuous Integration and Continuous Delivery. [Moved to: https://github.com/harness/gitness]
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
Jenkins - Jenkins automation server
dagster - An orchestration platform for the development, production, and observation of data assets.
gitlab-runner
n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.
github-act-runner - act as self-hosted runner
luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
Concourse - Concourse is a container-based continuous thing-doer written in Go.
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
onedev - Git Server with CI/CD, Kanban, and Packages. Seamless integration. Unparalleled experience.
Dask - Parallel computing with task scheduling