Project Overview The Restaurant Operations System (ROS) is a comprehensive synthetic data project designed to simulate one full year of restaurant operations. It includes interconnected datasets covering financials, orders, users, sales, deliveries, expenses, and more. This dataset is suitable for:
- System testing
- Data analytics and BI dashboards
- Process mining
- Data migration practice
- Training and demonstrations A total of 15 fully linked tables were generated using Excel formulas, business rules, Python scripts, and mapping logic.
Project Structure
- Data Generation Docket Describes how each table’s data is created, including:
- Data types
- Rules and constraints
- Formulas (Excel or Python)
- Faker-based fields
- Foreign key logic
- Data Migration Docket Defines:
- Source-to-target mapping
- Table relationships
- Transformation rules
- Validation and constraints Used for simulating a real data migration pipeline.
- Solution Design Docket Explains:
- Purpose of ROS
- Business objectives
- How synthetic data supports analytics
- Use cases such as profit analysis, cost reduction, staff efficiency improvement, and customer satisfaction evaluation
Tables Included (15 Total) Master Data: Clients, Restaurants, Subscriptions, Users, Roles, Departments, Currencies, Countries Transactional Data: Orders, Sales, Expenses, Deliveries Financial Data: Cash_up, Banking_Info Tax and Rules: TaxInfo Each table contains primary keys, foreign keys, formulas, constraints, and mapping rules.
Key Features
- One full year of simulated data
- UK and India-based restaurant distribution
- Tax logic for both countries
- Order and sales calculation formulas
- Delivery partner payout and reconciliation logic
- Cash-up and banking reconciliation
- Subscription user limit enforcement
- Role and department-based user management
- Franchise and non-franchise restaurant logic
Tech Stack Used
- Excel (data generation, formulas, logic building)
- Python (computed fields and automation logic)
- Faker library (synthetic personal data)
- Data modeling concepts
- ETL and migration techniques
- Knime
- PowerBI
Use Cases
- AI/ML dataset creation
- Data analytics and dashboards
- End-to-end data migration projects
- Finance and operational analytics
- ETL workflow practice
Business Objectives Addressed in the Dataset
- 15% profit increase analysis
- 20% operational cost reduction planning
- 25% staff efficiency improvement mapping
- Customer satisfaction structure for 90% positive outcomes
- Compliance and role-based data security model
Files Included
- Data Generation Docket
- Data Migration Docket
- Solution Design Docket
- ROS Project Execution Plan (Excel)
How to Use
- Review the Data Generation Docket to understand table logic.
- Review the Data Migration Docket for mapping rules.
- Review the Solution Design Docket for business understanding.
- Use the Excel file to generate or modify the dataset.
- Use the generated dataset for analytics, ETL, dashboards, or project demonstrations.