📊 SQL Data Warehouse Project

🧠 Overview

This project is a hands-on implementation of concepts from The Data Warehouse Toolkit (3rd Edition) by Ralph Kimball.
It recreates real-world scenarios using fictional datasets to demonstrate how dimensional modeling works in practice.

The goal is to bridge the gap between theory and implementation by building a simplified, yet realistic, data warehouse environment.

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🎯 Objectives

• Apply core data warehousing concepts in a practical context
• Design a dimensional model (star schema)
• Work with realistic, but fictional, business data
• Build a structured database optimized for analytical queries
• Translate textbook examples into executable SQL

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🏗️ Architecture

The project follows a simplified data warehouse structure focused on analytical modeling:

• Central fact tables capturing business events
• Surrounding dimension tables providing descriptive context
• Clear separation between raw data and analytical structures

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🧩 Data Modeling

This project applies Kimball’s dimensional modeling principles:

• Fact tables for measurable business processes
• Dimension tables such as customers, products, dates, etc.
• Use of surrogate keys
• Handling of Slowly Changing Dimensions (SCD)

Example schema

• fact_sales
• dim_customer
• dim_product
• dim_date
• dim_store

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📊 Data

All datasets used in this project are synthetic and created for educational purposes.
They are designed to mimic real-world business scenarios while remaining simple and easy to understand.