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Case study — Data Engineering · ETL
RepoEnterprise ETL & Inventory Pipeline
540K+ records, scheduled processing, threshold alerts
FastAPI
PostgreSQL
Supabase
SQLAlchemy
Celery
Redis
How it works
extract → transform → validate → load → alert
live01
Extract
inventory feeds
pull raw
02
Transform
Pandas · normalize
clean
03
Validate
integrity checks
guard
04
Load
Supabase / PostgreSQL · 540K+
bulk
05
Alert
Celery + Redis scheduled
watch
Async workers (Celery + Redis) stage each record through every gate; only validated rows reach Postgres, and anomalies fire real-time threshold alerts.
01The problem
Bulk-loading half a million inventory records reliably, keeping scheduling repeatable, and surfacing operational anomalies before they become stockouts.
02Approach
- 01Split the work into maintainable stages: ingestion, transformation, validation, persistence, and alerting.
- 02Used Pandas + SQLAlchemy to clean and bulk-load 540K+ records into Supabase/PostgreSQL.
- 03Scheduled processing with Celery + Redis so the pipeline runs repeatably, not ad-hoc.
- 04Generated real-time alerts when inventory crossed configured thresholds.
03Outcomes
- 540K+ records bulk-loaded into Supabase/PostgreSQL.
- Structured, repeatable data operations for daily inventory runs.
- Asynchronous task processing keeps ingestion off the request path.