Amazing: Your
Real-World
E-Commerce
Data Playground
Amazing is a ready-to-use synthetic dataset that mimics the experience of a real e-commerce platform. Ideal for recommendation engines, checkout flow testing, dynamic pricing, and customer behavior analysis.
Overview
Amazing is a realistic synthetic dataset that mirrors the full journey of a large e-commerce platform — from browsing and cart activity to purchases, payments, and returns. It captures the complete digital retail lifecycle, designed to help data scientists, engineers, and analysts experiment, test, and build smarter commerce experiences. Whether you're working on recommendation engines, checkout flows, dynamic pricing, or customer behavior analysis, Amazing gives you a safe, structured playground without any privacy concerns.
The dataset includes everything from customers and products to vendors, orders, and transaction histories. It's perfect for building and testing collaborative filtering systems, purchase prediction models, and inventory management tools. Whether you're preparing for interviews, building a data pipeline, or simulating retail behavior, Amazing helps you explore real-world scenarios with clean, comprehensive e-commerce platform data.
Full E-Commerce Lifecycle
Simulates a complete e-commerce experience, including user onboarding, product discovery, cart management, orders, and fulfillment.
Built for Development & Testing
Excellent for building and testing recommendation systems, purchase prediction models, and demand forecasting pipelines.
Backend Feature Testing
Useful for developers working on catalog management, order processing, returns, refunds, and customer support flows.
Rich Transaction Data
Supports use cases in user engagement analysis, customer segmentation, retention modeling, and promotional impact studies.
Analytics & Research
Suitable for SQL practice, ETL development, inventory planning, trend visualization, and A/B testing simulations.
How it Works
AI-Generated & Fully Synthetic
The Amazing dataset is generated using advanced AI agents, creating a realistic yet entirely synthetic representation of e-commerce interactions with zero real-world or personally identifiable data.
Realistic Simulation with Privacy
It simulates customer accounts, product browsing, cart activity, order placement, payments, and returns — all informed by industry-standard trends and public e-commerce consumption patterns.
High-Quality & Safe for Use
Built using insights from global e-commerce platforms and public data patterns, the dataset delivers structured, high-quality data suitable for recommendation systems, analytics dashboards, and machine learning model training.
Dataset Schema
A comprehensive relational model representing a modern e-commerce marketplace engineered for deep analysis and complex querying.
Users
Contains user account details, including login credentials, personal info, and user type.
Products
Stores product details like names, prices, descriptions, and stock quantities.
Categories
Organizes products into categories and subcategories for better navigation.
Orders
Tracks customer orders, including status, total amounts, and shipping addresses.
Payments
Records payment transactions for orders, including method, status, and amount.
Cart
Manages a user's shopping cart with selected items before purchase.
Cart Items
Stores individual products added to a user's cart with quantity and details.
Order Items
Stores product details in an order, including quantity and price at time of purchase.
Wishlists
Manages user-created wishlists for future purchase items and wishlist items linking products.
Reviews
Stores product reviews, including ratings and descriptions, for user feedback.
Coupons
Stores discount codes, expiration dates, and discount percentages for promotions.
Addresses
Stores user shipping addresses, linking them to orders for delivery.
- CSV
- JSON
- Excel
- MySQL
- PostgreSQL
- SQL Server
- Snowflake