🏗️ SQL Data Warehouse Project

An end-to-end SQL Data Warehouse built using Microsoft SQL Server, implementing the Medallion Architecture (Bronze, Silver, Gold layers) to transform raw data into business-ready insights for analytics and reporting.

🏛️ Architecture Overview

The data warehouse follows the Medallion Architecture, consisting of three logical layers:

📊 Solution Architecture

The system is designed using a layered architecture that separates raw ingestion, data transformation, and business-ready reporting data. This ensures scalability, maintainability, and high data quality.

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🥉 Bronze Layer – Raw Data

The Bronze layer acts as the landing zone for all incoming raw data from source systems.

Responsibilities

• Load raw data from CRM and ERP systems
• Preserve data in its original format
• Support full data refresh loads
• Store historical raw data without transformation

Objects

• Tables
• Stored Procedures

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🥈 Silver Layer – Cleaned & Standardized Data

The Silver layer processes and transforms raw data into a clean and consistent format.

Responsibilities

• Data cleansing and standardization
• Handling missing values and duplicates
• Data validation and quality checks
• Applying business rules and transformations

Objects

• Tables
• Stored Procedures

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🥇 Gold Layer – Business Data

The Gold layer contains business-ready, analytics-optimized datasets.

Responsibilities

• Data modeling for analytics
• Aggregations and KPI creation
• Business logic implementation
• Reporting-ready datasets

Objects

• Fact Tables
• Dimension Tables
• Views
• Star Schema

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🔄 Data Flow

CRM / ERP Sources
        ↓
Bronze Layer (Raw Data)
        ↓
Silver Layer (Cleaned Data)
        ↓
Gold Layer (Business Data)
        ↓
BI Tools / SQL Analysis / Reporting

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🛠️ Technologies Used

• Microsoft SQL Server
• SQL (T-SQL)
• SQL Server Management Studio (SSMS)
• Stored Procedures
• Views
• ETL Concepts
• Data Warehousing Principles
• Star Schema Modeling

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✨ Key Features

• End-to-end ETL pipeline
• Medallion architecture implementation
• Multi-source data ingestion (CRM & ERP)
• Data cleansing & transformation layers
• Business-ready reporting model
• Scalable warehouse design
• Star schema-based modeling

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📂 Project Structure

SQL-Data-Warehouse/
│
├── datasets/
│   ├── crm/
│   └── erp/
│
├── scripts/
│   ├── bronze/
│   ├── silver/
│   ├── gold/
│
├── docs/
│   └── DWH_Architecture.png
│
└── README.md

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📈 Business Value

This data warehouse enables organizations to:

• Centralize enterprise data
• Improve data quality and consistency
• Enable faster reporting and analytics
• Support data-driven decision making
• Build scalable BI solutions

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🚀 Future Enhancements

• Incremental data loading
• Change Data Capture (CDC)
• Automated ETL pipelines (SQL Agent)
• Power BI dashboard integration
• Cloud migration (Azure Synapse / ADF)

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👨‍💻 Author

Ajay Kumar