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Data Pipeline Orchestrator
Build an intelligent agent that manages ETL processes, monitors data quality, and automatically handles exceptions and edge cases in your data pipeline.
Overview
This advanced blueprint guides you through building an AI-powered data pipeline orchestrator that can intelligently manage complex ETL workflows, detect anomalies, and self-heal from common failure scenarios.
Build Time
4-8 weeks
Complexity
Advanced
Stack
Python, Airflow
Agents
2-3 agents
Use Cases
- Automated ETL workflow management
- Real-time data quality monitoring
- Intelligent error handling and recovery
- Pipeline performance optimization
Architecture Components
Pipeline Scheduler
Manages job scheduling, dependencies, and execution order.
Data Quality Monitor
Validates data integrity, detects schema drift, and identifies anomalies.
Exception Handler
Analyzes failures, attempts automatic remediation, and escalates when needed.
Performance Optimizer
Monitors resource usage and suggests optimizations for pipeline efficiency.
Recommended Tools
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