---
id: snowfakery
version: "4.2.1"
license: BSD 3-Clause License
license_treatment: permissive
maintenance: active
---

# snowfakery — Snowfakery is a tool for generating fake data that has relations between tables. Every row is faked data, but also unique and random, like a snowflake.

License: permissive · Maintenance: active · Downloads: 237.1K/mo

## 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 above — 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

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

- License: BSD 3-Clause License (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 237.1K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

fake data generation with relations, synthetic test data generator, yaml-based data faker, salesforce test data, database seeding tool, relational fake data, snowfakery recipe, test-data, salesforce, data-generation

[View on SkillFed](https://skillfed.io/packages/snowfakery) · [View on PyPI](https://pypi.org/project/snowfakery/)