Global Sales Data Engineering & Analytics 📊

Project Overview

This project demonstrates a complete data lifecycle, from designing a relational database in SQL Server to implementing automated ETL processes and generating business insights through an interactive Sales Dashboard. It focuses on analyzing global sales performance (EU and Non-EU regions) for the year 2023.

Dashboards preview

🛠️ Tech Stack

• Database Management: SQL Server (T-SQL)
• ETL Automation: Stored Procedures & Bulk Insert
• Data Visualization: Interactive Dashboards
• Languages: SQL

🏗️ Data Architecture

The DataSales database follows a structured approach using a Gold Schema to organize high-quality, reporting-ready data:

• Fact Tables: eu_orders, non_eu_orders (Central transaction data)
• Dimension Tables: Customers, Locations, and Products (Contextual data)

Eu Tables

Automated ETL Pipeline

I developed a robust stored procedure, gold.usp_BulkInsertData, to streamline the data ingestion process. This pipeline features:

• Transaction Management: Ensures data integrity by rolling back if errors occur.
• Environment Localization: Configured to handle regional formats (SET LANGUAGE Spanish) to avoid decimal and date conversion errors during the bulk load.
• Scalability: The procedure is parameterized to load different CSV sources efficiently.

📈 Key Insights (2023 Performance)

The final analysis reveals a strong growth year for the company:

• Revenue Growth: Total sales reached $733K, reflecting a 20.4% increase Year-Over-Year (YoY).
• Profitability: Net profit rose by 12.5%. The most profitable customer was Raymond Buch, contributing $6,781 in profit.
• Operational Opportunities: While sales are high, the Tables, Supplies, and Bookcases subcategories are currently operating at a loss, indicating a need for pricing or logistics optimization.

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🚀 Getting Started

1. Run innit_database.sql in your SQL Server instance to create the schema and tables.
2. Update the file paths in innit_tablesdata.sql to point to your local CSV data.
3. Execute the script to trigger the automated bulk loading process.

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📂 Repository Structure

data-warehouse-project/
│
├── dashboard/                          # Raw datasets used for the Allegheny County tax liens project
│   ├── images/                         # Dashboard images
│   │   └── ...
│   └── Sales Dashboards.twbx           # Tableau Dashboard File
│
├── datasets/                           # Raw datasets used for the project
│   ├── eu/
│   │   ├── Customers.csv
│   │   ├── Location.csv
│   │   ├── Orders.csv   
│   │   └── Products.csv
│   └── non-eu/                         # Dashboard images
│       ├── Customers.csv
│       ├── Location.csv
│       ├── Orders.csv   
│       └── Products.csv
│
├── docs/                               # Project documentation and architecture details
│   └── ...
│
├── scripts/                            # SQL scripts for ETL and transformations
│   ├── innit_database.sql              # Scripts for create database
│   └── innit_tablesdata.sql            # Scripts for load all tables
│
├── README.md                           # Project overview and instructions
├── LICENSE                             # License information for the repository
└── .gitignore

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🛡️ License

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

👨‍💻 About Me

Hi! I'm Leandro Gallo, a Systems Engineering student from Argentina with a strong interest in:

• Data Engineering
• Data Analytics
• Backend Development
• Cybersecurity
• Software Development

I enjoy building data pipelines, automation tools, and data-driven systems, and I am currently developing projects to strengthen my skills in data architecture, SQL development, and analytics.

This repository is part of my technical portfolio, where I showcase projects related to:

• Data Warehousing
• ETL Pipelines
• Data Modeling
• Analytics

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🔗 Connect With Me

📧 Email
leandrogallo698@gmail.com