📊 SQL Data Warehouse Analytics Project

This repository contains a comprehensive set of SQL scripts designed to build and analyze a data warehouse for business insights. It follows a structured approach, from database setup to dimensional modeling, and advanced analytical queries.

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🏗️ 0. Initialization – 00_init_database.sql

Creates the DataWarehouseAnalytics database and the gold schema. It sets up three core tables:

• dim_customers
• dim_products
• fact_sales

⚠️ Warning: This script drops the database if it exists. Ensure backups before running.

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🔍 1. Database Exploration – 01_database_exploration.sql

• Lists all tables and views in the database using INFORMATION_SCHEMA.TABLES
• Describes the structure of the dim_customers table using INFORMATION_SCHEMA.COLUMNS

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📐 2. Dimensions Exploration – 02_dimensions_exploration.sql

Explores dimension tables:

• Unique country values in dim_customers
• Unique combinations of category, subcategory, and product_name in dim_products

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📅 3. Date Range Analysis – 03_date_range_exploration.sql

Assesses date-related insights:

• Minimum and maximum order_date values from fact_sales
• Age range of customers based on birthdate

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📏 4. Measures & KPIs – 04_measures_exploration.sql

Calculates high-level metrics such as:

• Total sales, quantity, and orders
• Average selling price
• Number of unique products and active customers
• Business summary report via UNION ALL

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📊 5. Magnitude Analysis – 05_magnitude_analysis.sql

Groups and quantifies metrics:

• Customers by country and gender
• Products by category
• Average cost per category
• Revenue by product category and customer
• Distribution of sold items across countries

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🏅 6. Ranking Analysis – 06_ranking_analysis.sql

Identifies top and bottom performers using RANK(), TOP, and ROW_NUMBER():

• Top 5 revenue-generating products
• Bottom 5 worst-performing products
• Top 10 highest-spending customers
• Bottom 3 customers by number of orders

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📂 Folder Structure

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├── 00_init_database.sql
├── 01_database_exploration.sql
├── 02_dimensions_exploration.sql
├── 03_date_range_exploration.sql
├── 04_measures_exploration.sql
├── 05_magnitude_analysis.sql
└── 06_ranking_analysis.sql

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💡 Requirements

• Microsoft SQL Server
• Management tool (e.g., SSMS)
• CSV data

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