pathway
by pathwaycomPython ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.
In simple words: awesome-etl is a free book and learning collection — you read it, you do not install it, for learning new skills and knowledge at your own pace. It is completely free and open source — its code is public on GitHub, where 3,600 people have bookmarked it as worth using.
💡 A real-life example: A shop's messy sales spreadsheet goes into a tool like this, and comes out as one clear chart showing which product actually makes money.
RepoEasy wrote this simple explanation from the project's public data — its category, topics and description — so a beginner can understand it before downloading anything. For exact technical details, the project's own README (linked below) is the final word.
A curated list of awesome ETL frameworks, libraries, and software.
This is the project's own description from GitHub — we show it as-is rather than inventing details.
This project is listed under Data Pipelines in Data & Analytics, a category made for analysts, businesses, researchers and data-curious users.
Do I need coding knowledge? Basic Technical Setup. (Estimate based on project type: Learning/resource-style project or tagged for learners on GitHub. Always confirm from the repo's README.)
The source code is released under the CC0-1.0 license. Open-source licenses like this usually allow free use, including commercial use — but always read the actual license file in the repository before relying on it. Note: the code being free does not mean hosted services, APIs or infrastructure around it are free.
Activity level: Maintained · Last code update: 01 May 2026 · Created: 07 Oct 2015 · Open issues: 18
Official links: Releases ↗ · Documentation (README) ↗
We never publish installation steps we have not verified — the README above is the source of truth.
⭐ 3600 stars · 🍴 380 forks · 🐛 18 open issues · 💻 Language: — · 📄 License: CC0-1.0 · Default branch: main
Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.
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