Real-Time Inventory Analytics Pipeline
Client Partner: SwiftRetail Ltd · Build Timeline: 5 Months
Project Tech Stack
Allocated Engineering Pod
- Data Engineersx2
- Warehouse Architectx1
"We now adjust our supply orders based on live sales metrics."
Albert Gomez
Director of Logistics
1. Context & Business Goal
The Core Challenge: SwiftRetail's marketing dashboards loaded slowly, delaying supply chain inventory adjustments.
The Agreed Objective: Build a high-volume data pipeline to clean and load sales inventory metrics into a central warehouse.
2. Technical Research & Discovery
Analyzed database formats to select optimal warehouse storage structures. Prior to writing any code, our team compiled reference data frameworks and mapped API endpoints to identify speed limits.
3. System Architecture Design
Layout Scheme: Analytics dashboards displaying active store sales and warehouse stock levels.
Database Infrastructure Setup: Snowflake database fed by Apache Spark pipelines with transformation models managed via dbt.
4. Production Implementation Lifecycle
ETL Job Architecture
Structured Spark jobs to extract transactional logs from local databases.
dbt Modeling
Wrote SQL transformation models to compile clean inventory tables.
5. Validated Business Outcomes
- ✓Dashboards load in under 1 second, providing live sales metrics.
- ✓Automatic stock notifications reduced inventory shortages by 32%.