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📊 Data Warehouse and Analytics Project

Welcome to the Data Warehouse and Analytics Project repository! 🚀

This project demonstrates how to build a complete data warehouse and analytics solution using SQL (MySQL Workbench), following the Medallion Architecture (Bronze, Silver, Gold) and applying ETL best practices.

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🏗️ Data Architecture

This project follows the Medallion Architecture with three layers:

• Bronze Layer: Stores raw data ingested from CSV source files (ERP and CRM systems) into the staging area as-is.

• Silver Layer: Cleansed and standardized data. Includes removing duplicates, handling nulls, applying transformations, and building normalized tables.

• Gold Layer: Business-ready, analytical models in a Star Schema with dimension and fact tables. This layer supports reporting and dashboards.

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📖 Project Overview

This project showcases:

1. Data Architecture: Designing a data warehouse using the Medallion approach.
2. ETL Pipelines: Loading and transforming raw CSV data into the data warehouse.
3. Data Modeling: Creating fact and dimension tables for analytics.
4. Analytics & Reporting: Querying business metrics such as sales, product performance, and customer insights.

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🎯 Skills Demonstrated

This repository is perfect for demonstrating skills in:

• SQL Development (MySQL)
• Data Engineering & ETL
• Data Modeling (Star Schema)
• Analytics & Reporting
• Data Architecture Design

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🛠️ Tools & Datasets

• MySQL Workbench: For database design, ETL scripts, and transformations.
• CSV Datasets: ERP and CRM sales data used as raw inputs.
• Draw.io (diagrams): For architecture and data modeling diagrams.
• Git & GitHub: Version control and project hosting.

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🚀 Project Requirements

** Data Engineering - Data Warehouse**

• Data Sources: Two systems:

  • ERP System (Product, Sales details, Location data)
  • CRM System (Customer information)

• ETL:

  • Load data into Bronze Layer (raw tables).
  • Cleanse, standardize, and integrate data in Silver Layer.
  • Create Star Schema models (Fact & Dimensions) in Gold Layer.

• Data Quality:

  • Remove duplicates, fill missing values, and unify reference data.

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📂 Repository Structure

data-warehouse-project/
│
├── datasets/                           # Raw datasets (ERP and CRM data)
│
├── docs/                               # Documentation & diagrams
│   ├──DW - data_architecture.png        # Data warehouse architecture diagram
│   ├── DW -data_integration.png             
│   ├── ETL2.png                # ETL data flow diagram
│   ├── connecting tables.png          )
│          
│
├── scripts/                            # SQL scripts for each layer
│   ├── bronze/                         # Raw load scripts
│   ├── silver/                         # Cleansing & transformation scripts
│   ├── gold/                           # Analytical model (dimension & fact tables)
│       └── gold_views.sql              # Final views for gold layer
│
├── tests/                              # Data quality & validation scripts
│
├── README.md                           # Project documentation (this file)

DW - data_architecture.png DW -data_integration.png ETL2.png connecting tables.png

📜 Gold Layer (Star Schema)

The Gold Layer consists of:

1. gold_dim_customers – Customer dimension (demographics, location, etc.)
2. gold_dim_products – Product dimension (category, cost, product line)
3. gold_fact_sales – Fact table storing sales transactions

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📈 Example Insights

Using the Gold Layer, you can answer questions like:

• Who are our top customers by revenue?
• Which products and categories perform best?
• What are the monthly sales trends?
• Which countries or regions contribute the most sales?

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🛡️ License

This project is licensed under the MIT License. You are free to use, modify, and share it with attribution.

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