Multi-Tenant Data Pipeline System
Highly isolated, Airflow-driven ingestion system with automated LLM fault classification.
Problem
Managing data ingestion for multiple clients requires strict tenant-level isolation, scalable data partitioning, and robust error handling to prevent manual debugging bottlenecks.
Approach
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.
Outcome
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.
Technical Highlights
(7)- 01
JSON-driven DAG configurations enable strict tenant-level isolation without code changes
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Groq API integration automatically parses system logs and classifies pipeline failures
- 03
Two-phase execution flow validates AI-suggested fixes before applying them
- 04
Processes 100K+ records per day with tenant-isolated partitioning
- 05
New-tenant onboarding reduced to under 5 minutes
- 06
Mean pipeline resolution time cut from ~30 min to under 10 min
- 07
85%+ accuracy rate in automated failure classification