SQL Data Warehouse Development
Designed and implemented a production-style SQL Data Warehouse using the Medallion Architecture to transform raw CRM and ERP data into a clean, business-ready analytical model. The project demonstrates the complete ETL lifecycle, dimensional modeling, and data engineering practices used in modern analytics platforms.
Project Objective
Build a scalable SQL Data Warehouse capable of integrating multiple operational systems, improving data quality, and delivering business-ready datasets optimized for reporting and analytics.
Business Problem
CRM and ERP systems store business information independently, resulting in inconsistent formats, duplicate records, and fragmented reporting. The objective was to consolidate these sources into a unified analytical warehouse that supports reliable business intelligence.
Solution
- Implemented a three-layer Medallion Architecture.
- Loaded raw CRM and ERP data into the Bronze layer using BULK INSERT.
- Applied cleansing, validation, deduplication, and business rules within the Silver layer.
- Designed Star Schema dimension and fact models in the Gold layer.
- Created business-ready analytical views for downstream reporting.
Engineering Highlights
- Integrated multiple enterprise data sources.
- Developed reusable ETL pipelines using Stored Procedures.
- Applied data quality validation throughout the pipeline.
- Generated surrogate keys for dimensional modeling.
- Implemented historical product record handling.
- Designed optimized analytical Star Schema.
- Documented architecture, lineage, and warehouse design.
Business Value
- Created a single source of truth for enterprise reporting.
- Improved analytical query performance through dimensional modeling.
- Standardized customer and product information across systems.
- Produced reporting-ready datasets for Business Intelligence tools.
Skills Demonstrated
SQL Server • T-SQL • ETL Development • Data Warehousing • Medallion Architecture • Data Quality • Star Schema • Dimensional Modeling • Stored Procedures • Data Engineering