Large-Scale Traceability System

End-to-end supply chain traceability tracking 1M+ cartons/day across warehouse, transport, and RFID systems.

Overview

Designed and built an enterprise traceability platform to solve data fragmentation across global logistics networks. The system ingests high-velocity data from Warehouse Management Systems (WMS), Transport Management Systems (TMS), and RFID scanners to provide end-to-end visibility into carton movement, from warehouse shelf to final customer delivery.

Built on cloud workflow orchestration frameworks for ingestion and dbt for data transformation, with a partitioned cloud data warehouse as the analytical storage engine.

---

Architecture & Tech Stack

Layer	Technology
Sources	WMS, TMS, RFID scanners & IoT event streams
Ingestion	Managed Cloud Orchestration Workflow DAGs
Transformation	dbt (SQL incremental transformation models)
Storage	Cloud Data Warehouse (partitioned by event date, clustered by ID)
Monitoring	Automated SLA alerting and data contract validation

---

Challenges & Decisions

1. Reconciling Three Source Systems

• Problem: WMS, TMS, and RFID each had different schemas, event formats, and delivery guarantees.
• Approach: Standardized ingestion layer with source-specific adapters feeding a unified event schema in dbt staging models.
• Outcome: Single source of truth for carton lifecycle, regardless of origin system.

2. Scale at 1M+ Cartons/Day

• Problem: Naive queries over unpartitioned event logs were slow and expensive.
• Approach: Partitioned tables by event date and clustered by carton_id and source_system. Implemented dbt incremental models for efficient delta updates.
• Outcome: Query performance stayed under 5 seconds for most analytical queries while reducing cloud compute expenditure.

3. Self-Healing Reliability

• Problem: Operational supply chain systems cannot tolerate extended downtime or manual recovery intervention.
• Approach: Extensive workflow alerting, dbt data quality tests on every pipeline run, and idempotent DAGs that safely retry without duplicating records.
• Outcome: Zero production data loss or SLA breaches over the operational period.

---

Key Learnings

• Operability First: Designing for operability from day one is essential — if a pipeline cannot be debugged quickly during high-volume spikes, it is not ready for production.
• Incremental Transformation is Mandatory: dbt incremental models are essential at this scale — full refreshes would blow through compute budgets and miss SLA windows.
• Data Modeling over Pipeline Complexity: Cross-system traceability is fundamentally a data modeling problem, not just a pipeline problem. Getting the unified schema right simplifies downstream engineering immensely.