🤖 Agent-Orchestrated-ETL

Intelligent, Self-Optimizing Data Pipelines Powered by AI Agents

A revolutionary hybrid system combining Apache Airflow's robust workflow orchestration with LangChain's intelligent agent capabilities to create adaptive, self-healing data pipelines that learn and optimize automatically.

📋 Table of Contents

• Key Features
• Quick Start
• Architecture Overview
• Usage Examples
• CLI Commands
• Development Setup
• Contributing
• Documentation
• License

✨ Key Features

🧠 AI-Driven Pipeline Generation - Automatically analyzes data sources and generates optimal ETL workflows
🔄 Self-Healing Pipelines - Agents detect failures and implement recovery strategies autonomously
⚡ Dynamic Optimization - Real-time performance tuning based on execution patterns
🔌 Universal Connectors - Native support for S3, PostgreSQL, APIs, files, and more
📊 Intelligent Monitoring - Proactive issue detection with automated resolution suggestions
🚀 Production Ready - Enterprise-grade security, scaling, and compliance features
🎯 Autonomous SDLC - Self-improving system with progressive enhancement generations
⚡ Advanced Scaling - Multi-level caching, load balancing, and predictive auto-scaling
🛡️ Comprehensive Security - Multi-layer validation, encryption, and threat detection
🔬 Research-Ready - Built-in frameworks for ML experimentation and benchmarking
📡 Real-time Streaming - High-performance message processing with backpressure handling
🤖 AutoML Integration - Intelligent pipeline optimization with automated model selection
☁️ Cross-Cloud Federation - Seamless multi-provider deployment and orchestration
🛡️ Enhanced Security - Multi-layer threat detection with real-time validation
📈 Predictive Scaling - ML-powered resource optimization and cost management
👁️ Smart Observability - Anomaly detection with intelligent alerting and SLI/SLO monitoring

🚀 Quick Start

Prerequisites

• Python 3.8+
• Docker & Docker Compose (optional, for containerized setup)

Installation

# Clone the repository
git clone https://github.com/danieleschmidt/agent-orchestrated-etl.git
cd agent-orchestrated-etl

# Install dependencies
pip install -r requirements.txt

# Setup Airflow
export AIRFLOW_HOME=$(pwd)/airflow
airflow db init

# Initialize agent system
python setup_agent.py

# Start the system
airflow webserver --port 8080

Docker Setup (Recommended)

# Start all services
docker-compose up -d

# Access Airflow UI
open http://localhost:8080

🏗️ Architecture Overview

Our intelligent agent architecture consists of three core components:

• 🎯 Orchestrator Agent: Analyzes data sources and orchestrates optimal pipeline creation
• ⚙️ ETL Agents: Specialized agents handling extraction, transformation, and loading operations
• 👁️ Monitor Agent: Continuously monitors pipeline health and suggests performance optimizations

📖 Detailed Architecture Documentation

💡 Usage Examples

from agent_orchestrated_etl import DataOrchestrator

orch = DataOrchestrator()
pipeline = orch.create_pipeline(
    source="s3://my-bucket/data/",
)
pipeline.execute()

# override the load step
custom_pipeline = orch.create_pipeline(
    source="s3://my-bucket/data/",
    operations={"load": lambda data: print("loaded", data)},
)
custom_pipeline.execute()

# create a pipeline from a REST API
api_pipeline = orch.create_pipeline(
    source="api://example.com/endpoint",
)
api_pipeline.execute()

🖥️ CLI Commands

Execute pipelines directly from the command line with powerful options:

# Run pipeline with monitoring
run_pipeline s3 --output results.json --monitor events.log

# Preview pipeline without execution
run_pipeline s3 --list-tasks

# Generate Airflow DAG
generate_dag s3 dag.py --dag-id my_dag

# List available data sources
run_pipeline --list-sources

CLI Options:

• --list-tasks: Preview execution order without running
• --list-sources: Show supported data sources
• --monitor <file>: Capture task events to file
• --output <file>: Specify output location
• --dag-id <id>: Set custom DAG identifier

🔧 Development Setup

Install development dependencies and enable pre-commit hooks:

# Install in development mode with all dependencies
pip install -e .[dev]

# Setup pre-commit hooks for code quality
pre-commit install

# Run tests with coverage
pytest -q
coverage run -m pytest -q
coverage report -m

# Check code complexity
radon cc src/agent_orchestrated_etl -s -a

# Run linting and formatting
black . && isort . && flake8

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Quick Contributing Steps

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📚 Documentation

• 📖 Full Documentation
• 🏗️ Architecture Guide
• 🚀 Quick Start Guide
• 🔧 Development Guide
• 📋 API Reference
• 🗺️ Project Roadmap

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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Made with ❤️ by the Agent-Orchestrated-ETL team