🚀 DagLab

Supercharge your data workflows with paired Marimo notebooks for Dagster

Features • Quick Start • Documentation • Examples • Contributing

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🎯 What is DagLab?

DagLab revolutionizes data science workflows by seamlessly integrating Marimo notebooks with Dagster pipelines. Create, manage, and execute interactive notebooks that are directly paired with your Dagster assets and jobs, bringing the best of both worlds together.

🌟 Why DagLab?

• 🔄 Seamless Integration: Directly connect Marimo notebooks to Dagster assets
• ⚡ Real-time Development: Hot-reload notebooks while developing pipelines
• 🛠️ Production Ready: Enterprise-grade security, monitoring, and deployment
• 📊 Interactive Workflows: Visualize and interact with your data pipelines
• 🚀 Zero Configuration: Works out of the box with sensible defaults

✨ Features

📓 Smart Notebook Generation daglab scaffold --asset sales_forecast --template ml Pre-configured templates for common workflows Automatic Dagster integration Type-safe data handling Built-in performance tracking	🔍 Asset Discovery daglab discover --pattern "sales_*" --tags ml Find Dagster assets instantly Filter by patterns and tags Real-time GraphQL integration Beautiful Rich CLI output
🚦 Development Environment daglab dev --services all --monitor Integrated Marimo & Dagster servers Auto-restart on failures Real-time health monitoring Performance dashboards	📤 Export & Migration daglab export notebook.py --cloud s3://bucket daglab migrate *.ipynb --target marimo/ Multi-cloud storage support Jupyter to Marimo conversion Metadata preservation Batch processing

🚀 Quick Start

Installation

# Basic installation
pip install daglab

# With cloud support
pip install daglab[aws,gcp,azure]

# Full installation
pip install daglab[all]

Your First Workflow

# 1. Initialize in your Dagster project
daglab init

# 2. Create a notebook for your asset
daglab scaffold --asset my_model --template ml

# 3. Start development environment
daglab dev

# 4. Open the generated notebook and start coding!

📹 See it in action

$ daglab init
✨ Initializing DagLab in current directory...
📁 Created daglab.yaml configuration
📁 Created notebooks/ directory
✅ DagLab initialized successfully!

$ daglab scaffold --asset revenue_model --template ml
🎯 Generating notebook for asset: revenue_model
📝 Using template: ml (Machine Learning)
✨ Created: notebooks/assets/revenue_model.marimo.py
✅ Notebook generated successfully!

$ daglab dev
🚀 Starting development environment...
📊 Marimo server: http://localhost:2718
⚙️  Dagster UI: http://localhost:3000
✅ All services running! Press Ctrl+C to stop.

📚 Documentation

🎯 Core Commands

🏗️ Project Management

daglab init                    # Initialize DagLab in your project
daglab doctor                  # Check system health & auto-fix issues
daglab clean                   # Clean artifacts and caches

📓 Notebook Operations

daglab scaffold                # Generate paired notebooks
daglab list                   # List all notebooks
daglab run                    # Execute notebooks

🔍 Dagster Integration

daglab discover               # Find Dagster entities
daglab run --job daily_etl   # Run Dagster jobs
daglab run --asset my_model  # Materialize assets

🚀 Development & Production

daglab dev                    # Start development environment
daglab export                 # Export notebooks to various formats
daglab stats                  # View usage statistics
daglab migrate               # Convert Jupyter to Marimo

🛠️ Advanced Features

🔒 Enterprise Security

• 🛡️ Security Audit: Built-in vulnerability scanning
• 🔐 Authentication: Multi-provider auth support
• 📋 Compliance: GDPR, SOX, PCI DSS ready
• 🚨 Real-time Alerts: Security event monitoring

📊 Performance Monitoring

# Automatic performance tracking in notebooks
from daglab import track_performance

@track_performance
def process_data(df):
    # Your code here
    return transformed_df

View Performance Dashboard

daglab dev --dashboard
# Access at http://localhost:8080

📊 Performance Metrics:
├── Cell Execution Time: 2.3s
├── Memory Usage: 156MB
├── CPU Utilization: 45%
└── I/O Operations: 234

☁️ Multi-Cloud Support

# Export to different cloud providers
daglab export notebook.py --cloud s3://my-bucket/notebooks/
daglab export notebook.py --cloud gs://my-bucket/notebooks/
daglab export notebook.py --cloud az://my-container/notebooks/

🔄 CI/CD Integration

# .github/workflows/daglab.yml
name: DagLab CI
on: [push]
jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Run DagLab Tests
        run: |
          pip install daglab[dev]
          daglab doctor --fix
          daglab test

📈 Examples

📊 ETL Pipeline

# notebooks/assets/customer_etl.marimo.py
import marimo as mo
from daglab import dagster_asset, track_performance

@dagster_asset
@track_performance
def customer_data():
    """Extract and transform customer data."""
    df = load_customer_data()
    cleaned = clean_data(df)
    return cleaned

# Interactive visualization
mo.ui.table(customer_data())

🤖 ML Model Training

# notebooks/jobs/train_model.marimo.py
import marimo as mo
from daglab import dagster_job, visualize_metrics

@dagster_job
def train_revenue_model():
    """Train and evaluate ML model."""
    X_train, y_train = prepare_data()
    model = train_model(X_train, y_train)
    metrics = evaluate_model(model)
    
    # Visualize results
    visualize_metrics(metrics)
    return model

🤝 Contributing

We love contributions! See our Contributing Guide for details.

# Setup development environment
git clone https://github.com/your-org/daglab
cd daglab
pip install -e .[dev]

# Run tests
pytest tests/ -v

# Run security audit
python scripts/security/security_audit.py

🏆 Why Choose DagLab?

⚡ Fast 10x faster notebook development with hot-reload and caching

🔒 Secure Enterprise-grade security with automated vulnerability scanning

📈 Scalable From local development to distributed cloud deployment

🎨 Flexible Customizable templates and extensible architecture

📊 Performance

🗺️ Roadmap

• Core CLI functionality
• Dagster integration
• Marimo notebook support
• Cloud storage integration
• Performance monitoring
• Real-time collaboration
• AI-powered code suggestions
• Visual workflow builder
• Mobile app support

📄 License

DagLab is licensed under the Apache License 2.0. See LICENSE for details.

🙏 Acknowledgments

Built with ❤️ using:

• Dagster - The data orchestration platform
• Marimo - Reactive Python notebooks
• Typer - CLI framework
• Rich - Beautiful terminal output

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Documentation • Examples • Issues • Discussions

Made with ❤️ by the DagLab Team