# texera
**Repository Path**: mirrors_apache/texera
## Basic Information
- **Project Name**: texera
- **Description**: Collaborative Machine-Learning-Centric Data Analytics Using Workflows
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-08-07
- **Last Updated**: 2026-05-02
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
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.
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}
}
```