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SQL Data Warehouse Project 🚀

Based on the DataWithBaraa curriculum

Welcome to my SQL Data Warehouse project repository!

This project is a hands-on data engineering portfolio built following the DataWithBaraa course. It covers the full journey from raw CSV ingestion all the way to business-ready analytics using the Medallion Architecture on SQL Server.


🏗️ Data Architecture

This project follows the Medallion Architecture with three layers:

CSV Files (Source)
      ↓
🥉 Bronze Layer  →  Raw data as-is, no transformations
      ↓
🥈 Silver Layer  →  Cleaned, standardized, and normalized data
      ↓
🥇 Gold Layer    →  Business-ready Star Schema for reporting
Layer Purpose
Bronze Ingests raw CSV files using BULK INSERT. Data is stored as-is with no changes. Tables are truncated and reloaded on each run.
Silver Cleans and standardizes the bronze data. Handles NULLs, trims whitespace, normalizes values using CASE, applies business rules, and adds metadata columns.
Gold Models data into Fact and Dimension tables using a Star Schema. Optimized for analytical queries and reporting.

📋 Project Overview

This project covers:

  • Data Architecture — Designing a modern data warehouse using Medallion Architecture
  • ETL Pipelines — Extracting raw CSVs, transforming them through Silver, loading into Gold
  • Data Modeling — Building Fact and Dimension tables for analytical queries
  • Data Quality — Validating completeness, consistency, and accuracy at each layer
  • Analytics & Reporting — Writing SQL-based reports for business insights

🛠️ Tools & Resources

Everything used in this project is free!

Tool Purpose
SQL Server Express Lightweight database server to host the warehouse
SSMS (SQL Server Management Studio) GUI for writing and managing SQL scripts
Git & GitHub Version control and project management
DrawIO Designing data architecture and flow diagrams
Notion Project planning and documentation
DataWithBaraa Course and dataset source

📁 Repository Structure

sql-datawarehouse-project/
│
├── datasets/                        # Raw source CSV files
│
├── docs/                            # Project documentation
│   ├── data_architecture.drawio     # Medallion architecture diagram
│   ├── data_catalog.md              # Field descriptions and metadata
│   ├── data_flow.drawio             # End-to-end data flow diagram
│   ├── data_models.drawio           # Star schema model diagram
│   ├── naming_conventions.md        # Table, column, and file naming rules
│
├── scripts/                         # All SQL scripts
│   ├── bronze/                      # Raw ingestion scripts (BULK INSERT)
│   ├── silver/                      # Cleaning and transformation scripts
│   ├── gold/                        # Star schema and reporting model scripts
│
├── tests/                           # Data quality checks and test scripts
│
├── README.md                        # You are here
├── LICENSE                          # License info
├── .gitignore                       # Files ignored by Git
└── requirements.txt                 # Project dependencies

🎯 Project Requirements

Data Engineering — Building the Warehouse

Objective: Build a modern data warehouse using SQL Server to consolidate raw CRM and ERP source data, making it ready for analytical reporting.

Specifications:

  • Data Source: CRM and ERP system CSV files provided by DataWithBaraa
  • Data Quality: Cleanse and resolve quality issues before loading into Silver
  • Integration: Consolidate all source tables into a single, clean data model
  • Scope: Latest dataset only — no historization required
  • Documentation: Maintain a data catalog and clear naming conventions

Analytics & Reporting — Business Intelligence

Objective: Deliver SQL-based analytics to provide insights into:

  • 👥 Customer Behavior — Segmentation, demographics, purchase patterns
  • 📦 Product Performance — Top products, categories, returns
  • 📈 Sales Trends — Revenue over time, monthly comparisons, growth rates

🔗 References


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Modern SQL Data Warehouse project using Medallion Architecture (Bronze, Silver, Gold), ETL pipelines, and Star Schema modeling on SQL Server.

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