docs: add data pipeline tutorial for v0.1.2 (#427)
Summary
- Add step-by-step data pipeline tutorial at
doc/source/tutorials/data-pipeline.md- Covers the full workflow: cloud storage direct read → schema-on-read import → graph queries → Parquet export (local + cloud)
- All commands verified against NeuG v0.1.2
Closes #426
Content
- Install and load extensions (httpfs + parquet)
- Preview remote data (HTTPS + OSS scheme)
- Import nodes — one line, no DDL
- Import edges
- Graph queries
- Export query results to Parquet (local + cloud write-back)
- Convert to PyArrow Table
- Credential configuration reference (OSS / S3)
- Common pitfalls & FAQ
🤖 Generated with Claude Code
版权所有:中国计算机学会技术支持:开源发展技术委员会
京ICP备13000930号-9
京公网安备 11010802047560号
NeuG
NeuG (pronounced “new-gee”) is a graph database for HTAP (Hybrid Transactional/Analytical Processing) workloads. NeuG provides two modes that you can switch between based on your needs:
For more information on using NeuG, please refer to the NeuG documentation.
News
Installation
Please note that
neugrequiresPythonversion 3.8 or above. The package works on Linux, macOS, and Windows (via WSL2).For more detailed installation instructions, please refer to the installation guide.
Quick Example
Development & Contributing
For building NeuG from source and development instructions, see the Development Guide.
We welcome contributions! Please read our Contributing Guide before submitting issues or pull requests.
AI-Assisted Workflow
We apply an AI-assisted Spec-Driven workflow inspired by GitHub Spec-Kit. We provide convenient commands for contributions:
/create-issuecommand in your IDE, or submit an issue manually/create-prcommand in your IDE, or submit a PR manuallyFor more details, see the AI-Assisted Development Guide.
Acknowledgements
NeuG builds upon the excellent work of the open-source community. We would like to acknowledge:
License
NeuG is distributed under the Apache License 2.0.