MySQL | Power BI | Data Modeling | SQL Analytics
This project demonstrates an end-to-end business intelligence and data analytics workflow using MySQL and Power BI.
The objective was to transform raw pizza sales data into a clean, normalized relational database, validate business metrics using SQL, and deliver interactive Power BI dashboards to support data-driven decision-making.
The project reflects real-world practices in data preprocessing, normalization (3NF), SQL-based analysis, data modeling, and dashboard development.
- Analyze sales performance across pizzas, categories, and sizes
- Identify best- and worst-performing products
- Track daily and monthly order trends
- Validate Power BI metrics using SQL queries
- Present insights through executive-ready dashboards
- Database: MySQL
- Visualization: Power BI
- Query Language: SQL
- Modeling: Relational schema (3NF)
- Documentation: SQL process documented separately
- Design Assets: Custom icons and images
- Reviewed raw transactional sales data
- Cleaned inconsistencies and standardized formats
- Identified key entities and relationships
- Designed a relational schema normalized to Third Normal Form (3NF)
- Reduced redundancy and ensured data integrity
- Improved query efficiency and scalability
Key tables include:
ordersorder_detailspizzaspizza_types
SQL queries were written to compute and validate key business metrics, including:
- Total Revenue
- Total Orders
- Total Pizzas Sold
- Average Order Value
- Average Pizzas per Order
- Sales by Category and Size
- Daily and Monthly Order Trends
- Best and Worst Selling Products
All SQL commands and transformations were fully documented and used to cross-check Power BI results for accuracy.
- Connected MySQL database directly to Power BI
- Imported normalized tables
- Defined table relationships
- Performed light data transformations
- Created calculated columns and DAX measures
Two interactive dashboards were developed using Power BI:
- KPI cards for key metrics
- Daily and monthly order trends
- Sales distribution by category and size
- Interactive slicers for date range and pizza category
- Executive-level summary view
- KPI cards for key metrics
- Top 5 pizzas by:
- Revenue
- Quantity Sold
- Total Orders
- Bottom 5 pizzas by:
- Revenue
- Quantity Sold
- Total Orders
- Clear product-level performance comparison
Custom visuals, icons, slicers, and layout design were used to ensure clarity and usability.
- Classic pizzas generate the highest revenue and order volume
- Large-size pizzas contribute the most to total sales
- Order volume peaks during weekends and evening hours
- Sales increase significantly during July and January
- Certain products consistently underperform across multiple metrics
- All KPIs were calculated independently using SQL
- Equivalent measures were recreated in Power BI using DAX
- Results were cross-validated to ensure consistency and accuracy
- Execute SQL scripts in MySQL to create and populate normalized tables
- Connect MySQL to Power BI
- Load the Power BI .pbix file
- Use slicers to explore insights interactively
- Automate ETL using Python
- Add predictive sales forecasting
- Publish dashboards to Power BI Service
- Expand analysis with customer segmentation