snowfakery

What it is and what it does

Snowfakery is a data generation tool designed to create fake but realistic relational datasets from declarative YAML recipes. It generates unique, random rows across related tables while maintaining referential integrity—useful for testing, development, and populating Salesforce orgs. The tool depends on faker for base data generation, SQLAlchemy for database connectivity, and Jinja2 for template rendering within recipes.

You define what data to generate in a YAML recipe file, specifying tables, relationships, and field patterns. Snowfakery then produces output to stdout, any SQLAlchemy-compatible database, or directly to Salesforce when run through CumulusCI. The package is actively maintained, supports Python 3.11–3.13, and carries no known security vulnerabilities.

Use it for:

• Populate test databases with realistic relational data for integration tests without hardcoding fixtures
• Generate sample Salesforce orgs with linked records for UAT or demo environments
• Create anonymized or synthetic datasets for development when production data cannot be used
• Seed databases with consistent, reproducible fake data across multiple test runs
• Rapidly prototype data models by generating sample data matching a schema

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Snowfakery generates fake relational data from YAML recipes, writing output to stdout, databases via SQLAlchemy, or Salesforce orgs when embedded in CumulusCI.

Yes. Snowfakery is actively maintained, has no known vulnerabilities, installs with low friction, and solves a real problem—generating realistic relational test data from declarative recipes. It is particularly valuable if you work with Salesforce via CumulusCI or need repeatable synthetic datasets for testing. The BSD license imposes no practical restrictions.

Install

snowfakery on PyPI

pip install snowfakery

uv add snowfakery

poetry add snowfakery

Installing snowfakery

Before you install

Low friction install with a pure-Python wheel and 13 runtime dependencies. Actively maintained with a recent commit on 2026-04-27 and no known vulnerabilities.

License in practice

BSD 3-Clause License is permissive; you can use, modify, and distribute this package with minimal restrictions, provided you include the license notice.

Quickstart

pip install snowfakery

snowfakery generate recipe.yaml

Or in Python:
from snowfakery import generate_data
generate_data(recipe_file='recipe.yaml')

Requires Python 3.11 or later. Output to Salesforce requires CumulusCI integration.

Verify before relying

• Whether the package can generate data to custom output formats beyond stdout and SQLAlchemy databases without code modification
• Performance characteristics when generating large datasets or deeply nested relational structures

Package facts

Evidence: snowfakery-4.2.1-py3-none-any.whl

Keywords: data generation, fake data, salesforce, snowfakery

Tags

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