Plaitpy
A fake data modeler.
Overview
Plaitpy is a tool for generating synthetic data that has interesting and realistic patterns. It allows users to define data models using declarative YAML templates, which can specify the relationships between different fields and how they should be generated. Plaitpy is designed to be easy to use and flexible, and can be used to generate a variety of fake data for applications such as testing, validation, and performance tuning.
✨ Key Features
- Open-source Python program
- Declarative YAML templates for data modeling
- Composable and nested templates
- Support for sampling from CSV files
- Lambda expressions and conditional logic
- Integration with the Faker library
🎯 Key Differentiators
- Declarative YAML-based approach to data modeling
- Focus on generating data with interesting patterns and relationships
- Composable and extensible templates
Unique Value: Provides a simple and declarative way to generate fake data with realistic patterns and relationships, making it a useful tool for a variety of testing and validation tasks.
🎯 Use Cases (5)
✅ Best For
- Generating fake browser data, website traffic, and taxi-trip datasets
💡 Check With Vendor
Verify these considerations match your specific requirements:
- Users who are not comfortable with YAML or the command line
- Organizations that require a fully managed, enterprise-grade platform
🏆 Alternatives
Offers a more structured and model-driven approach to data generation compared to programmatic libraries like Faker, and is more flexible and powerful than simple web-based tools like Mockaroo.
💻 Platforms
✅ Offline Mode Available
🔌 Integrations
💰 Pricing
Free tier: Completely free and open-source.
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