Self-Healing Data Pipeline Orchestrator
Config-driven ETL orchestration with AI-assisted recovery
Overview
This project is a custom-built workflow orchestration framework designed specifically for large-scale, time-series data pipelines. The system prioritizes reliability, reproducibility, and minimal operational intervention by treating failures as first-class events rather than exceptions.
Pipeline Architecture
- Built a declarative, config-driven ETL framework to define ingestion, transformation, and validation steps
- Designed pipelines to be idempotent, replayable, and restart-safe across partial failures
- Implemented checkpointing at stage boundaries to allow precise resume points
- Supported time-series ingestion patterns with backfills, late data, and rolling windows
Self-Healing & Recovery
- Implemented automatic retries with exponential backoff and failure classification
- Persisted pipeline state to enable safe recovery without human intervention
- Isolated failures to individual stages to prevent cascading pipeline breakdowns
AI-Assisted Failure Analysis
- Applied AI-driven log analysis to cluster and classify recurring pipeline failures
- Detected anomalies in execution duration, row counts, and error patterns
- Generated actionable recovery suggestions instead of raw error messages