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📊 Data Analytics Dashboard - Gold Layer Project

SQL Server Power BI Database Architecture Business Intelligence GitHub

📝 Project Description

This project showcases the end-to-end development of a Business Intelligence ecosystem, focused on the Gold Layer (Business Layer) architecture. By transforming raw transactional data into high-performance analytical views, I engineered a robust foundation for strategic decision-making. The project concludes with an interactive dashboard that translates complex SQL logic into actionable insights regarding sales growth, product lifecycles, and customer demographics.

View the final result: > Dashboard Preview

🚀 Technical Features (SQL)

The engine of this project lies in the business logic implemented via SQL Server. Advanced views were created to automatic key metrics:

  • Year-Over-Year (YoY) Performance: Utilization of window functions (LAG, OVER) to calculate annual growth per product.
  • Cumulative Analysis: Implementation of Running Totals and Moving Averages to identify long-term sales trends.
  • Data Segmentation: Dynamic classification of products by cost ranges and customers by geographic location.
  • Participation Metrics: "Part-to-Whole" calculations to determine the revenue impact of each product category.

📂 View Structure (Gold Layer)

The main script generates the following logical tables:

Vista Propósito Analítico
gold.cumulative_analysis Historical trends and monthly cumulative totals.
gold.performance_analysis Comparison of current sales vs. average and previous year.
gold.part_to_analysis Contribution percentage by product category.
gold.data_segmentation Inventory grouping by cost ranges.
gold.customers_country Demographic distribution of the customer base.

More tables info:

tables doc

📈 Dashboard Insights

Based on the integration of SQL views with the visualization tool:

  1. Category Dominance: Bikes accounted for 96.46% of total sales, identifying them as the primary business driver.
  2. Temporary Growth: A significant peak in sales and customer acquisition is observed between 2013 and 2014.
  3. Cost Efficiency: 37.29% of products sold fall within the "Below 100" range, suggesting high sales volume for entry-level products.
  4. Global Distribution: North America stands out as the market with the highest customer density.

🛠️ Installation and Usage

  1. Clone the repository: 
       git clone https://github.com/leandrogallo-dev/data-analytics-dashboard.git

📂 Repository Structure

sanoyfresco-sales-analytics
│
├── dashboard/ # dashboard from power BI
├── datasets/  # Dataset used for the analysis
├── scripts/   # SQL scripts with analytical queries
│
├── docs/      # Project documentation
│
├── README.md  # Project documentation
└── LICENSE

🛡️ 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

🔗 Connect With Me

📧 Email
leandrogallo698@gmail.com