Zwiggy is a realistic, full-scale synthetic dataset built to mirror how a modern food delivery platform works. It’s designed for developers, data scientists, and learners who want structured data for testing machine learning models, dashboards, backend systems, and product prototypes — without the hassle of privacy issues. From users and restaurants to orders, deliveries, and payments, Zwiggy covers all the moving parts you'd expect in a real-world app.
Whether you're predicting order times, segmenting customers, or building SQL queries for interviews, Zwiggy is your go-to playground. You can simulate API flows, test payment logic, or analyze demand trends — all using timestamped, relational, and geo-tagged data. It's a hands-on toolkit for learning, experimenting, and building with confidence.
Highlights:
- Simulates full food delivery lifecycle with realistic business logic.
- Ideal for ML training, API testing, dashboard development, and performance benchmarking.
- Supports use cases in customer analytics, logistics, pricing, and retention modeling.
- Suitable for academic projects, bootcamps, technical interviews, and prototyping.
- Includes well-structured tables for users, orders, deliveries, restaurants, payments, and reviews.
The Zwiggy dataset simulates a modern food delivery platform, capturing key elements like restaurants, menus, customer profiles, orders, payments, and delivery logistics. It’s a great resource for practicing SQL, designing backend systems, building machine learning models, and running data analytics workflows. The schema includes tables with realistic relationships and constraints, allowing for a comprehensive understanding of the food delivery business.
Key tables in the dataset include:
- Cuisines: Stores cuisine types available on the platform.
- Customer: Captures customer profiles and links to orders and reviews.
- Delivery Partner: Tracks delivery personnel, their availability, and ratings.
- Restaurant: Contains restaurant details like name, contact info, and ratings.
- Menu Item: Stores information about food items offered by restaurants.
- Cart: Represents customer shopping carts and associated metadata.
- Cart Items: Tracks menu items added to carts, including quantities.
- Orders: Stores details about customer orders, including status and total price.
- Order Items: Breaks down individual food items in each order.
- Payments: Logs payment transactions, methods, and status.
- Delivery Details: Tracks delivery logistics and assigned delivery partners.
- Reviews: Contains customer feedback and ratings on menu items.
- User Address: Stores customer address information for deliveries.
The Zwiggy dataset was generated using advanced AI agents, ensuring a realistic yet synthetic representation of food delivery transactions. This dataset is entirely artificially created and does not contain any real-world or personally identifiable data. Our AI-driven approach involves simulating customer behaviors, restaurant operations, order placements, and delivery logistics based on real-world patterns while maintaining complete privacy and ethical data generation practices. The dataset was designed to mimic real scenarios by leveraging machine learning models trained on publicly available trends and industry insights. This ensures that users can work with structured, high-quality data without legal or privacy concerns. Whether for data analysis, software testing, or AI model training, this dataset serves as a valuable resource while being 100% synthetic and responsibly generated.
The Zwiggy dataset is designed for maximum flexibility, offering access across a variety of environments to support analysts, developers, and researchers. Whether you're conducting data analysis, building machine learning pipelines, or integrating with applications, Zwiggy ensures a smooth and efficient experience. The dataset is available in commonly used formats, compatible with multiple relational databases, and also accessible via a modern cloud data warehouse. This versatility allows seamless integration into local systems, cloud workflows, and enterprise-scale projects.
- Available file formats: CSV, JSON, Excel
- Available databases: MySQL, PostgreSQL, SQL Server
- Cloud database access: Snowflake
