- Scala 47.4%
- TypeScript 37.3%
- Python 8.2%
- HTML 2.2%
- Shell 1.5%
- Other 3.3%
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### What changes were proposed in this PR? Clarifies the scope of the security model in `SECURITY.md`: the document considers only Kubernetes deployments, and Docker Compose and single-node deployments are not intended for production. ### Any related issues, documentation, discussions? None. ### How was this PR tested? Docs-only change; no code touched. ### Was this PR authored or co-authored using generative AI tooling? Generated-by: Claude Code (Claude Opus 5.5) 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com> |
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Apache Texera - Human-AI Collaborative Data Science Using Visual Workflows
Apache Texera (Incubating) is an open-source platform for human-AI collaborative data science using visual workflows.
Official Site
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Blog
Apache Texera (Incubating) is an open-source platform for human-AI collaborative data science using visual workflows. It enables human analysts to construct, execute, and refine data analysis tasks through an intuitive GUI, assisted by AI agents that understand natural-language instructions. Texera is well suited for a wide range of applications, including “AI for Science,” by making advanced AI and data science capabilities accessible to a broader community. It can run on a laptop for local use or be deployed in the cloud to support scalable processing of large datasets.
The platform has the following key features:
- Natural-language data science through AI agents
- Intuitive GUI-based workflows for data science
- Real-time collaboration for workflow editing and execution
- Runtime debugging and interactive workflow execution
- Language-agnostic workflow runtime, native support for Python and Java
- Parallel backend engine for scalable big-data processing
- Separation of compute and storage for flexible cloud deployment
Citation
Please cite Texera as
@article{DBLP:journals/pvldb/WangHNKALLDL24,
author = {Zuozhi Wang and
Yicong Huang and
Shengquan Ni and
Avinash Kumar and
Sadeem Alsudais and
Xiaozhen Liu and
Xinyuan Lin and
Yunyan Ding and
Chen Li},
title = {Texera: {A} System for Collaborative and Interactive Data Analytics
Using Workflows},
journal = {Proc. {VLDB} Endow.},
volume = {17},
number = {11},
pages = {3580--3588},
year = {2024},
url = {https://www.vldb.org/pvldb/vol17/p3580-wang.pdf},
timestamp = {Thu, 19 Sep 2024 13:09:37 +0200},
biburl = {https://dblp.org/rec/journals/pvldb/WangHNKALLDL24.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
