Case Study
FDA MAUDE Data Processing Platform
ETL platform that ingests FDA MAUDE reports, validates data quality and powers analytics with traceable lineage.
PythonFastAPIPostgreSQLRedisCeleryDocker
Problem Statement
FDA MAUDE data arrives as noisy, irregular text with inconsistent fields. The goal was to normalize ingestion, enforce validation and enable reliable downstream analysis.
Dataset / Scale
Millions of device event records, daily incremental updates, 50+ schema fields with strict validation rules.
Architecture Diagram
Ingestion workers fetch daily MAUDE archives
Schema validation + retry queue with structured logging
PostgreSQL storage + materialized views for analytics
Audit trail for every transform and rejection
ML / LLM Pipeline
Download → Parse → Normalize → ValidateDeduplicate → Enrich → StoreAggregate → Analytics ready views
System Design
- FastAPI ingestion service for control plane
- Celery workers for parallel ETL
- Redis backed task queue and caching
- PostgreSQL with partitioned tables
Engineering Challenges
- Handling malformed entries without breaking ingestion
- Ensuring idempotent pipeline re-runs
- Keeping validation latency under 500ms per batch
Metrics
- 99.2% valid record rate after normalization
- 40% reduction in reprocessing time
- 3x faster analytics query response
Screenshots
Ingestion monitoring dashboard with validation status
Schema compliance report with error breakdown
Analytics view of device event trends