Human-AI Collaborative Data Science Using Visual Workflows

Clone this repo:
  1. ada1cfe feat(workflow-operator): keep the rows Distinct's engine keeps for NaN and signed zero (#8975) by Kary Zheng · 6 hours ago main
  2. 448dead feat(frontend): pin an operator's result beside it, with a tab per output port (#9063) by Kary Zheng · 6 hours ago
  3. cc1f201 fix(workflow-form): let the read-only step panel scroll (#9407) by yangzhang75 · 15 hours ago
  4. 03a5bb8 feat(workflow-form): bring the selected step's result to the top, and give each result a name and an eye (#9334) by yangzhang75 · 31 hours ago
  5. a0ab2dc fix(pyamber): keep peek side effect free (#8960) by carloea2 · 33 hours ago

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

texera-screenshot

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} }