---
title: Lineage Graph Accelerator
emoji: 🔥
colorFrom: purple
colorTo: blue
sdk: gradio
sdk_version: 6.0.0
app_file: app.py
pinned: true
license: mit
short_description: AI data lineage extraction & export to data catalogs
tags:
  - data-lineage
  - mcp
  - gradio
  - data-governance
  - dbt
  - airflow
  - etl
  - mcp-in-action-track-productivity
  - hackathon
---

# Lineage Graph Accelerator 🔥

**AI-powered data lineage extraction and visualization for modern data platforms**

[![HuggingFace Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-blue)](https://huggingface.co/spaces/aamanlamba/Lineage-graph-accelerator)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Gradio](https://img.shields.io/badge/Gradio-6.0.0-orange)](https://gradio.app)

> 🎉 **Built for the Gradio Agents & MCP Hackathon - Winter 2025** 🎉
>
> Celebrating MCP's 1st Birthday! This project demonstrates the power of MCP integration for enterprise data governance.

---

## 🌟 What is Lineage Graph Accelerator?

Lineage Graph Accelerator is an AI-powered tool that helps data teams:

- **Extract** data lineage from dbt, Airflow, BigQuery, Snowflake, and more
- **Visualize** complex data dependencies with interactive Mermaid diagrams
- **Export** lineage to enterprise data catalogs (Collibra, Microsoft Purview, Alation)
- **Integrate** with MCP servers for enhanced AI-powered processing

### Why Data Lineage Matters

Understanding where your data comes from and where it goes is critical for:
- **Data Quality**: Track data transformations and identify issues
- **Compliance**: Document data flows for GDPR, CCPA, and other regulations
- **Impact Analysis**: Understand downstream effects of schema changes
- **Data Discovery**: Help analysts find and trust data assets

---

## 🎯 Key Features

### Multi-Source Support
| Source | Status | Description |
|--------|--------|-------------|
| dbt Manifest | ✅ | Parse dbt's manifest.json for model dependencies |
| Airflow DAG | ✅ | Extract task dependencies from DAG definitions |
| SQL DDL | ✅ | Parse CREATE statements for table lineage |
| BigQuery | ✅ | Query INFORMATION_SCHEMA for metadata |
| Custom JSON | ✅ | Flexible node/edge format for any source |
| Snowflake | 🔄 | Coming via MCP integration |

### Export to Data Catalogs
| Catalog | Status | Format |
|---------|--------|--------|
| OpenLineage | ✅ | Universal open standard |
| Collibra | ✅ | Data Intelligence Platform |
| Microsoft Purview | ✅ | Azure Data Governance |
| Alation | ✅ | Data Catalog |
| Apache Atlas | 🔄 | Coming soon |

### Visualization Options
- **Mermaid Diagrams**: Interactive, client-side rendering
- **Subgraph Grouping**: Organize by data layer (raw, staging, marts)
- **Color-Coded Nodes**: Distinguish sources, tables, models, reports
- **Edge Labels**: Show transformation types

---

## 🚀 Quick Start

### Try Online (HuggingFace Space)

1. Visit [Lineage Graph Accelerator on HuggingFace](https://huggingface.co/spaces/YOUR_SPACE)
2. Click "Load Sample" to load example data
3. Click "Extract Lineage" to see the visualization
4. Explore the Demo Gallery for more examples

### Run Locally

```bash
# Clone the repository
git clone https://github.com/YOUR_REPO/lineage-graph-accelerator.git
cd lineage-graph-accelerator

# Create virtual environment
python3 -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Run the app
python app.py
```

Open http://127.0.0.1:7860 in your browser.

---

## 📖 Usage Guide

### 1. Text/File Metadata Tab

Paste your metadata directly:

```json
{
  "nodes": [
    {"id": "source_db", "type": "source", "name": "Source Database"},
    {"id": "staging", "type": "table", "name": "Staging Table"},
    {"id": "analytics", "type": "table", "name": "Analytics Table"}
  ],
  "edges": [
    {"from": "source_db", "to": "staging"},
    {"from": "staging", "to": "analytics"}
  ]
}
```

### 2. Sample Data

Load pre-built samples to explore different scenarios:
- **Simple JSON**: Basic node/edge lineage
- **dbt Manifest**: Full dbt project with 15+ models
- **Airflow DAG**: ETL pipeline with 15 tasks
- **Data Warehouse**: Snowflake-style multi-layer architecture
- **ETL Pipeline**: Complex multi-source pipeline
- **Complex Demo**: 50+ node e-commerce platform

### 3. Export to Data Catalogs

1. Extract lineage from your metadata
2. Expand "Export to Data Catalog"
3. Select format (OpenLineage, Collibra, Purview, Alation)
4. Click "Generate Export"
5. Copy the JSON for import into your catalog

---

## 🔌 MCP Integration

Connect to MCP (Model Context Protocol) servers for enhanced processing:

```
┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│  Lineage Graph  │────▶│   MCP Server    │────▶│   AI Model      │
│   Accelerator   │     │  (HuggingFace)  │     │   (Claude)      │
└─────────────────┘     └─────────────────┘     └─────────────────┘
```

### Configuration

1. Expand "MCP Server Configuration" in the UI
2. Enter your MCP server URL
3. Add API key (if required)
4. Click "Test Connection"

### Run Local MCP Server

```bash
uvicorn mcp_example.server:app --reload --port 9000
```

Then use `http://localhost:9000/mcp` as your server URL.

---

## 🏗️ Architecture

```mermaid
flowchart TD
    A[User Interface - Gradio] --> B[Input Parser]
    B --> C{Source Type}
    C -->|dbt| D[dbt Parser]
    C -->|Airflow| E[Airflow Parser]
    C -->|SQL| F[SQL Parser]
    C -->|JSON| G[JSON Parser]
    D & E & F & G --> H[LineageGraph]
    H --> I[Mermaid Generator]
    H --> J[Export Engine]
    I --> K[Visualization]
    J --> L[OpenLineage]
    J --> M[Collibra]
    J --> N[Purview]
    J --> O[Alation]

    subgraph Optional
        P[MCP Server] --> H
    end
```

### Project Structure

```
lineage-graph-accelerator/
├── app.py                 # Main Gradio application
├── exporters/             # Data catalog exporters
│   ├── __init__.py
│   ├── base.py           # Base classes
│   ├── openlineage.py    # OpenLineage format
│   ├── collibra.py       # Collibra format
│   ├── purview.py        # Microsoft Purview format
│   └── alation.py        # Alation format
├── samples/               # Sample data files
│   ├── sample_metadata.json
│   ├── dbt_manifest_sample.json
│   ├── airflow_dag_sample.json
│   ├── sql_ddl_sample.sql
│   ├── warehouse_lineage_sample.json
│   ├── etl_pipeline_sample.json
│   └── complex_lineage_demo.json
├── mcp_example/          # Example MCP server
│   └── server.py
├── tests/                # Unit tests
│   └── test_app.py
├── memories/             # Agent configuration
├── USER_GUIDE.md         # Comprehensive user guide
├── BUILD_PLAN.md         # Development roadmap
└── requirements.txt
```

---

## 🧪 Testing

```bash
# Activate virtual environment
source .venv/bin/activate

# Run unit tests
python -m unittest tests.test_app -v

# Run setup validation
python test_setup.py
```

---

## 📋 Requirements

- Python 3.9+
- Gradio 5.49.1+
- See `requirements.txt` for full dependencies

---

## 🎖️ Competition Submission

**Track**: Track 2 - MCP in Action (Productivity)

**Team Members**:

- [Aaman Lamba](https://aamanlamba.com) | [HuggingFace](https://huggingface.co/aamanlamba) | [GitHub](https://github.com/aamanlamba)

### Judging Criteria Alignment

| Criteria | Implementation |
|----------|----------------|
| **UI/UX Design** | Clean, professional interface with tabs, accordions, and color-coded visualizations |
| **Functionality** | Full MCP integration, multiple input formats, 5 export formats |
| **Creativity** | Novel approach to data lineage visualization with AI-powered parsing |
| **Documentation** | Comprehensive README, USER_GUIDE.md, inline comments |
| **Real-world Impact** | Solves critical enterprise need for data governance and compliance |

### Demo Video

📺 **YouTube**: [Watch the Demo](https://youtu.be/U4Dfc7txa_0)
🎥 **Loom**: [Alternative Link](https://www.loom.com/share/3de27e88e01f4e97bfd13e4f0031f416)

**Highlights**:

- AI Assistant with Google Gemini generating lineage from natural language
- MCP Integration with Local Demo server
- Demo Gallery with 50+ node complex pipelines
- Export to Collibra, Purview, and Apache Atlas
- Interactive Mermaid visualizations with zoom and download

### Social Media Post

📱 **LinkedIn**: [View the announcement post](https://www.linkedin.com/posts/aamanlamba_lineage-graph-accelerator-a-hugging-face-activity-7400658296166297600-n9a6)

---

## 🔜 Roadmap

- [x] Gradio 6 upgrade for enhanced UI components
- [x] Agentic chatbot for natural language queries (Google Gemini)
- [x] Apache Atlas export support
- [ ] File upload functionality
- [x] Graph export as PNG/SVG
- [ ] Batch processing API
- [ ] Column-level lineage

---

## 🤝 Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

---

## 📄 License

MIT License - see [LICENSE](LICENSE) for details.

---

## 🙏 Acknowledgments

- **Anthropic** - MCP Protocol and Claude
- **Gradio Team** - Amazing UI framework
- **HuggingFace** - Hosting and community
- **dbt Labs** - Inspiration for metadata standards
- **OpenLineage** - Open lineage specification

---

## 📞 Support

- **Documentation**: [USER_GUIDE.md](USER_GUIDE.md)
- **Author Website**: [aamanlamba.com](https://aamanlamba.com)
- **Issues**: [GitHub Issues](https://github.com/aamanlamba/lineage-graph-accelerator/issues)
- **Discussion**: [HuggingFace Community](https://huggingface.co/spaces/aamanlamba/Lineage-graph-accelerator/discussions)

---

<p align="center">
  Built with ❤️ by <a href="https://aamanlamba.com"><strong>Aaman Lamba</strong></a> for the <strong>Gradio Agents & MCP Hackathon - Winter 2025</strong>
  <br>
  Celebrating MCP's 1st Birthday! 🎂
</p>