Advanced SQL Techniques for E-commerce Analytics Dashboard

An interactive dashboard showcasing advanced SQL techniques for analyzing e-commerce sales and finance data with stunning visualizations.

Features

• Window Functions: Monthly sales growth analysis
• Subqueries: Revenue vs targets comparison
• Common Table Expressions (CTEs): Sales by category and region
• Recursive Queries: Cumulative revenue analysis
• Performance Optimization: Query performance improvement metrics
• Employee Performance Analysis: Quarterly performance tracking

Technologies Used

• HTML5
• CSS3
• JavaScript (ES6+)
• Chart.js for data visualization
• Bootstrap 5 for UI components
• SQL (PostgreSQL/MySQL compatible)

Setup Instructions

Prerequisites

• A modern web browser
• (Optional) A local server (like python -m http.server or live-server)
• (Optional) A database server (PostgreSQL/MySQL) for real data integration

Steps

1. Clone the repository:

   git clone https://github.com/your-username/advanced-sql-ecommerce-analytics.git
   cd advanced-sql-ecommerce-analytics
   

2. Open index.html in your web browser or run a local server:

# Using Python
python -m http.server 8000

# Then navigate to http://localhost:8000

3. (Optional) To use with a real database:

• Set up a PostgreSQL or MySQL database
• Execute the schema in sql/schema.sql
• Import sample data from sql/sample_data.sql
• Modify the JavaScript to fetch data from your database via an API

Usage

The dashboard provides an interactive interface to explore various SQL techniques applied to e-commerce data:

1. Key Metrics: Overview of important business metrics at the top.
2. Window Functions: Line chart showing monthly sales and growth rates.
3. Subqueries: Bar chart comparing revenue against targets by category.
4. CTEs: Grouped bar chart showing sales by product category and customer segment.
5. Recursive Queries: Line chart showing quarterly and cumulative revenue.
6. Performance Optimization: Bar chart showing query execution time before and after optimization.
7. Employee Performance: Radar chart comparing employee performance against targets.

Each section includes:

• A description of the SQL technique
• Code snippets
• Interactive visualization
• Key business insights

Future Improvements

• Connect to a real database via a backend API
• Implement user authentication and role-based access
• Add more interactive features (e.g., date range selectors)
• Include additional SQL techniques and visualizations
• Optimize for mobile devices

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Chart.js for the powerful charting library
• Bootstrap for the responsive UI components