Zwiggy: Your
Real-World
Food Delivery
Data Playground
Zwiggy leverages advanced generative techniques to create a high-fidelity food delivery ecosystem. Ideal for ML training, system testing, and product prototyping.
Overview
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.
End-to-End Simulation
Simulates full food delivery lifecycle with realistic business logic.
Built for Development & Testing
Ideal for ML training, API testing, dashboard development, and performance benchmarking.
Advanced Use Cases
Supports use cases in customer analytics, logistics, pricing, and retention modeling.
Perfect for Learning & Practice
Suitable for academic projects, bootcamps, technical interviews, and prototyping
Structured & Scalable Data Model
Includes well-structured tables for users, orders, deliveries, restaurants, payments, and reviews.
How it Works
AI-Generated & Fully Synthetic
he Zwiggy dataset is generated using advanced AI agents, creating a realistic yet entirely synthetic representation of food delivery transactions with zero real-world or personally identifiable data.
Realistic Simulation with Privacy
It simulates customer behavior, restaurant operations, order flows, and delivery logistics based on real-world patterns while ensuring complete privacy and ethical data generation practices.
High-Quality & Safe for Use
Built using insights from public trends and industry data, the dataset delivers structured, high-quality data suitable for analysis, testing, and AI training—without any legal or privacy concerns.
Dataset Schema
A comprehensive 10-table relational model engineered for deep analysis and complex querying.
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.
Restaurants
Contains restaurant details like name, contact info, and ratings.
Orders
Stores information about food items offered by restaurants.
Order Items
Breaks down individual food items in each order.
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.
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.
- CSV / JSON / Parquet
- SQL Dump (PostgreSQL)
- NoSQL Ready (MongoDB)
- PostgreSQL / MySQL
- Snowflake / BigQuery
- Apache Spark
- AWS S3 Buckets
- Google Cloud Storage
- Azure Blob Storage