redun
Airflow
| redun | Airflow | |
|---|---|---|
| 4 | 169 | |
| 486 | 34,485 | |
| 2.1% | 2.3% | |
| 7.5 | 10.0 | |
| about 2 months ago | 5 days ago | |
| Python | 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.
redun
- Redun: Yet another redundant workflow engine
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Nextflow: Data-Driven Computational Pipelines
I'm personally a huge fan of redun¹ for running computational pipelines. It's pure python, it's easy to learn/debug, it has automatic caching, retry, provenance logging, and a great integration with AWS Batch for running large jobs. I've been really impressed with how easy it is to run a job to completion that fans out to thousands of AWS spot instances at once.
I've used nextflow in the past, and I've found it to be much harder to use. Learning another DSL is annoying, documentation was sparse, I constantly ran into bugs, and it was hard to debug in general. I don't know how much it's changed over the past 3 years though.
¹https://github.com/insitro/redun
- Insitro's redun: Yet another redundant workflow engine
- Insitro's new open source software uses DAGs.
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?
Prefect - The easiest way to build, run, and monitor data pipelines at scale.
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.
nextflow - A DSL for data-driven computational pipelines
dagster - An orchestration platform for the development, production, and observation of data assets.
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.
n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.
cgpipe - cgpipe - minimum viable HPC pipeline
common-workflow-language - Repository for the CWL standards. Use https://cwl.discourse.group/ for support 😊
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
common-workflow-
Dask - Parallel computing with task scheduling