🏢 SQL Data Warehouse Project

📖 Overview

This project focuses on designing and implementing a modern data warehouse using SQL Server. It consolidates sales data from multiple sources into a clean, analytics-ready data model that supports business reporting and decision-making.

It covers both data engineering (building the warehouse 🛠️) and data analytics (deriving insights using SQL 📊).

---

🏗️ Data Warehouse Design (Data Engineering)

🎯 Objective

Design and develop a modern SQL Server–based data warehouse that integrates sales data from multiple sources, enabling reliable analytical reporting and informed business decisions.

📝 Specifications

• 💾 Data Sources
  Import sales data from two source systems (ERP and CRM), provided as CSV files.

• 🧹 Data Quality
  Cleanse data and resolve quality issues before loading into the warehouse.

• 🔗 Data Integration
  Merge data from all source systems into a unified, analytics-friendly data model optimized for queries.

• ⏱️ Scope
  Focus on the most recent snapshot of the data. Historical tracking and slowly changing dimensions are not required.

• 📚 Documentation
  Provide clear and structured documentation of the data model for both business users and analytics teams.

---

📊 Analytics & Reporting (Data Analytics)

🎯 Objective

Develop SQL-based analytical queries to generate meaningful business insights from the data warehouse.

🔑 Key Analysis Areas

• 👥 Customer Behavior
  Analyze purchasing patterns and customer activity.

• 🛍️ Product Performance
  Evaluate product sales, revenue contribution, and performance trends.

• 📈 Sales Trends
  Identify sales patterns over time to support strategic planning.

These analytics provide stakeholders with key metrics for data-driven decisions.

---

🏗️ Data Architecture

The data architecture for this project follows Medallion Architecture Bronze, Silver, and Gold layers:

1. Bronze Layer: Stores raw data as-is from the source systems. Data is ingested from CSV Files into SQL Server Database.
2. Silver Layer: This layer includes data cleansing, standardization, and normalization processes to prepare data for analysis.
3. Gold Layer: Houses business-ready data modeled into a star schema required for reporting and analytics.

🛠️ Technology Stack

• SQL Server
• Azure Data Studio (SQL client 💻)
• CSV-based source data 📄

---

🚀 How to Run

1. Set up SQL Server in your local environment.
2. Load source CSV files into staging tables.
3. Execute ETL scripts to build the data warehouse 🏗️.
4. Run analytical SQL queries to explore insights 📊.

---

📂 Repository Structure

data-warehouse-project/
│
├── datasets/                           # Raw datasets used for the project (ERP and CRM data)
│
├── docs/                               # Project documentation and architecture details
│   ├── etl.drawio                      # Draw.io file shows all different techniquies and methods of ETL
│   ├── data_architecture.drawio        # Draw.io file shows the project's architecture
│   ├── data_catalog.md                 # Catalog of datasets, including field descriptions and metadata
│   ├── data_flow.drawio                # Draw.io file for the data flow diagram
│   ├── data_models.drawio              # Draw.io file for data models (star schema)
│   ├── naming-conventions.md           # Consistent naming guidelines for tables, columns, and files
│
├── scripts/                            # SQL scripts for ETL and transformations
│   ├── bronze/                         # Scripts for extracting and loading raw data
│   ├── silver/                         # Scripts for cleaning and transforming data
│   ├── gold/                           # Scripts for creating analytical models
│
├── tests/                              # Test scripts and quality files
│
├── README.md                           # Project overview and instructions
├── LICENSE                             # License information for the repository
├── .gitignore                          # Files and directories to be ignored by Git
└── requirements.txt                    # Dependencies and requirements for the project

---

⚖️ License

This project is licensed under the MIT License 📝.

---