Stop wasting time writing data scripts. Mockingbird generates millions of realistic, interconnected records with referential integrity in seconds. No more customer_id: 999 pointing to nowhere.
# Define your data structure
Users:
count: 1000
fields:
user_id: {generator: sequence}
name: {generator: faker, config: {generator: name}}
email: {generator: faker, config: {generator: email}}
Orders:
count: 5000
fields:
order_id: {generator: sequence}
user_id: {generator: ref, config: {ref: Users.user_id}}
total: {generator: faker, config: {generator: pydecimal}}Time Reduction
Records in 30s
Reproducible
Powerful features that transform how you create test data
Generate 100,000+ records in under 30 seconds. Built for performance with optimized algorithms and efficient data generation.
Foreign keys that actually work. Automatic dependency resolution ensures referential integrity across all your entities.
Powered by Faker library with support for names, addresses, dates, and complex business logic through expressions.
Same seed = same data, every time. Perfect for consistent testing and debugging across teams.
Export to CSV, Parquet, or JSON. Choose the format that works best for your workflow and data analysis needs.
Define your data structure in clean YAML. No complex scripting - just describe what you want.
Consistent test fixtures
Large-scale datasets
Realistic API responses
Development environments
Professional presentations
Edge case scenarios
Install Mockingbird via pip and generate your first dataset
Then create your first blueprint:
mockingbird initmockingbird generate blueprint.yaml