This project analyzes large-scale restaurant sales data (11M+ records) to uncover key business insights and support data-driven decision-making.
The solution combines Databricks, SQL, and Power BI to handle big data processing and deliver interactive dashboards.
- Power BI (DAX, Data Modeling, Dashboards)
- SQL (Data Querying & Transformation)
- Databricks (Big Data Processing)
- 7 CSV files
- 2 JSON files
- Total size: 11M+ records
- Ingested and integrated data from multiple sources (CSV & JSON)
- Cleaned and transformed large-scale datasets using Databricks
- Queried and structured data using SQL
- Built a data model and interactive dashboard in Power BI
- Multi-page interactive dashboard (5 pages)
- KPIs: Total Sales, Orders, Items Sold, Average Rating
- Dynamic filters (Branch, Category, Payment Method, Date)
- Sales analysis by category, branch, and year
- Payment method distribution analysis
- Peak sales occur between 6 PM and 10 PM
- Cairo branch is the top-performing location
- Cash is the most frequently used payment method
- Customer ratings mostly range between 3 and 4
- Sales are uneven across branches
- Negative prices detected in some transactions
- Sales drop during early hours
- High dependency on cash payments
- Low-rated orders require further investigation
- Focus marketing campaigns during peak evening hours
- Improve low-rated products and customer experience
- Promote digital payments to reduce cash dependency
- Optimize staffing during peak hours
- Introduce offers during low-traffic periods
- Enabled performance comparison across branches
- Identified top-selling categories and customer behavior
- Supported data-driven decision-making
- Highlighted opportunities for operational improvement
This project demonstrates end-to-end data analysis on big data, from raw data processing to building interactive dashboards and generating actionable business insights.