Multimodal | Cloud-Native | AI-Ready | Large-Scale

Data-Juicer (DJ) transforms raw data chaos into AI-ready intelligence. It treats data processing as composable infrastructure—providing modular building blocks to clean, synthesize, and analyze data across the entire AI lifecycle, unlocking latent value in every byte.

Whether you're deduplicating web-scale pre-training corpora, curating agent interaction traces, or preparing domain-specific RAG indices, DJ scales seamlessly from your laptop to thousand-node clusters—no glue code required.

Alibaba Cloud PAI has deeply integrated Data-Juicer into its data processing products. See Quickly submit a DataJuicer job.

🧃 Juicer, our natural-language data-refinement model, turns cleaning instructions, filtering rules, and semantic-tagging requirements into structured outputs. Try it on HuggingFace or ModelScope, or see the Juicer docs.

Zero-install exploration:

• JupyterLab Playground with Tutorials
• Ask DJ Copilot

Install & run:

uv pip install py-data-juicer
dj-process --config demos/process_simple/process.yaml

Or compose in Python:

from data_juicer.core.data import NestedDataset
from data_juicer.ops.filter import TextLengthFilter
from data_juicer.ops.mapper import WhitespaceNormalizationMapper

ds = NestedDataset.from_dict({
    "text": ["Short", "This passes the filter.", "Text   with   spaces"]
})
res_ds = ds.process([
    TextLengthFilter(min_len=10),
    WhitespaceNormalizationMapper()
])

for s in res_ds:
    print(s)

• 200+ operators spanning text, image, audio, video, and multimodal data
• Recipe-first: Reproducible YAML pipelines you can version, share, and fork like code
• Composable: Drop in a single operator, chain complex workflows, or orchestrate full pipelines
• Hot-reload: Iterate on operators without pipeline restarts

• Foundation Models: Pre-training, fine-tuning, RL, and evaluation-grade curation
• Agent Systems: Clean tool traces, structure context, de-identification, and quality gating
• RAG & Analytics: Extraction, normalization, semantic chunking, deduplication, and data profiling

• Scale: Process 70B samples in 2h on 50 Ray nodes (6400 cores)
• Efficiency: Deduplicate 5TB in 2.8h using 1280 cores
• Optimization: Automatic OP fusion (2-10x speedup), adaptive parallelism, CUDA acceleration, robustness
• Observability: Built-in tracing for debugging, auditing, and iterative improvement

⭐ If Data-Juicer saved you time or improved your data work, please consider starring the repo. It helps more people discover the project and keeps you notified of new releases and features.

View All Release and News Archive

The below list focuses on developer-facing integration and usages in alphabetical order. Missing your project / name? Feel free to open a PR or reach out.

Data-Juicer plugs into your existing stack and evolves with community contributions:

• data-juicer-agents — DJ Copilot and agentic workflows
• data-juicer-hub — Community recipes and best practices
• data-juicer-sandbox — Data-model co-development with feedback loops

AgentScope · Apache Arrow · Apache HDFS · Apache Hudi · Apache Iceberg · Apache Paimon · Alibaba PAI · Delta Lake · DiffSynth-Studio · EasyAnimate · Eval-Scope · Huawei Ascend · Hugging Face · LanceDB · LLaMA-Factory · ModelScope · ModelScope Swift · NVIDIA NeMo · Ray · RM-Gallery · Trinity-RFT · Volcano Engine

Alibaba Group, Ant Group, BYD Auto, ByteDance, DTSTACK, JD.com, NVIDIA, OPPO, Xiaohongshu, Xiaomi, Ximalaya, and more.

CAS, Nanjing University, Peking University, RUC, Tsinghua University, UCAS, Zhejiang University, and more.

We believe in building together. Whether you're fixing a typo, crafting a new operator, or sharing a breakthrough recipe, every contribution shapes the future of data processing.

We welcome contributions at all levels:

• Good First Issues — Add operators, improve docs, report issues, or fix bugs
• Developer Guide — Optimize engines, add features, or enhance core infrastructure
• DJ-Hub — Share knowledge: recipes, papers, and best practices
• Connect: Slack · DingTalk · Discord

Discord	DingTalk

Data-Juicer is made possible by the users and community:

• Initiated by: Alibaba Tongyi Lab
• Co-developed with: Alibaba Cloud PAI, Anyscale (Ray team), Sun Yat-sen University, NVIDIA (NeMo team), and contributors worldwide
• Inspired by: Apache Arrow, Ray, Hugging Face Datasets, BLOOM, RedPajama-Data, ...

For detailed documentation, please see here.

Quick Links:

• operator zoo — Browse 200+ operators with examples
• Agent interaction quality & bad-case — In-repo recipe, JSONL pipeline, HTML report (demos/agent/; operators such as agent_bad_case_signal_mapper are also listed in docs/Operators.md)
• data-juicer-hub — Community-driven recipes and best practices
• developer guide — Build your own code and contribute to DJ
• data-juicer-cookbook — resource archive
• awesome_llm_data — “Awesome List” for data-model co-development

Data-Juicer is released under the Apache License 2.0. Attribution is appreciated: please use our badge, or text as "This project uses Data-Juicer: https://github.com/datajuicer".

If you find Data-Juicer useful in your work, please cite:

@inproceedings{djv1,
  title={Data-Juicer: A One-Stop Data Processing System for Large Language Models},
  author={Chen, Daoyuan and Huang, Yilun and Ma, Zhijian and Chen, Hesen and Pan, Xuchen and Ge, Ce and Gao, Dawei and Xie, Yuexiang and Liu, Zhaoyang and Gao, Jinyang and Li, Yaliang and Ding, Bolin and Zhou, Jingren},
  booktitle={SIGMOD},
  year={2024}
}

@article{djv2,
  title={Data-Juicer 2.0: Cloud-Scale Adaptive Data Processing for and with Foundation Models},
  author={Chen, Daoyuan and Huang, Yilun and Pan, Xuchen and Jiang, Nana and Wang, Haibin and Zhang, Yilei and Ge, Ce and Chen, Yushuo and Zhang, Wenhao and Ma, Zhijian and Huang, Jun and Lin, Wei and Li, Yaliang and Ding, Bolin and Zhou, Jingren},
  journal={NeurIPS},
  year={2025}
}