SQL Data Warehouse & Analytics Project preview 1

Key Features & Highlights

Medallion Architecture — Built Bronze, Silver, and Gold layers for structured data processing.

ETL Pipeline — Created SQL stored procedures to load, clean, and transform data.

Data Cleaning — Cleaned, standardized, validated, and removed duplicate data.

Star Schema — Designed customer and product dimensions with a sales fact table.

SQL Analytics — Performed KPI, trend, YoY, ranking, and segmentation analysis.

Customer & Product Reports — Created RFM customer analysis and product performance reports.

Technologies & Architecture

Microsoft SQL ServerSQL Server Management Studio (SSMS)T-SQLMedallion ArchitectureETL / ELTDimensional ModelingStar SchemaStored ProceduresCTEs & Window FunctionsData Cleaning & Transformation

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SQL Data Warehouse & Analytics Project

About Project

A learning-focused end-to-end SQL Data Warehouse project built with Microsoft SQL Server to explore data engineering, ETL, data transformation, dimensional modeling, and business intelligence concepts.

The project integrates multi-source CRM and ERP datasets using a Medallion Architecture (Bronze → Silver → Gold). Raw CSV data is ingested and preserved in the Bronze layer, cleansed and standardized in the Silver layer, and transformed into a business-friendly Star Schema in the Gold layer for analytical reporting. The project also includes exploratory analysis of sales, customers, products, trends, segmentation, and RFM metrics using advanced SQL.

Data Warehouse Development

I built this project as a learning exercise to understand how an end-to-end SQL Data Warehouse is designed and implemented using Microsoft SQL Server. I loaded CRM and ERP datasets into the Bronze layer, applied data cleansing, standardization, deduplication, validation, and transformation in the Silver layer, and created the Gold layer using dimensional modeling with customer and product dimensions and a sales fact table.

I also implemented reusable SQL stored procedures for the ETL process and practiced handling real-world data quality issues such as inconsistent identifiers, categorical values, invalid dates, and duplicate records. This helped me understand how raw multi-source data can be transformed into reliable, analytics-ready datasets.

What I Learned

Through this project, I gained practical experience in SQL Server, T-SQL, ETL development, Medallion Architecture, data cleansing and transformation, dimensional modeling, Star Schema design, stored procedures, and advanced SQL analytics. I learned how to integrate data from multiple sources and use techniques such as CTEs, window functions, ranking, running totals, moving averages, YoY analysis, RFM segmentation, and SCD Type 2 to build meaningful analytical reports.