Multi-Tenant Data Pipeline System
Highly isolated, Airflow-driven ingestion system with automated LLM fault classification.
Managing data ingestion for multiple clients requires strict tenant-level isolation, scalable data partitioning, and robust error handling to prevent manual debugging bottlenecks.
Built a multi-tenant ETL system utilizing Apache Airflow and PostgreSQL with isolated, JSON-driven DAG configurations. Integrated the Groq API to automatically parse system logs and classify pipeline failures, enforcing a two-phase execution flow to validate AI suggestions.
Successfully processed over 100K records per day while cutting new-tenant onboarding time to under 5 minutes. Reduced mean pipeline resolution time from roughly 30 minutes to under 10 minutes with an 85%+ accuracy rate in failure classification.
Key Technical Highlights
JSON-driven DAG configurations enable strict tenant-level isolation without code changes
Groq API integration automatically parses system logs and classifies pipeline failures
Two-phase execution flow validates AI-suggested fixes before applying them
Processes 100K+ records per day with tenant-isolated partitioning
New-tenant onboarding reduced to under 5 minutes
Mean pipeline resolution time cut from ~30 min to under 10 min
85%+ accuracy rate in automated failure classification