Snowfakery
Snowfakery generates synthetic data from YAML recipe files, including records linked across tables.
What Snowfakery is
Snowfakery generates synthetic data from YAML recipe files, including records linked across tables. Recipes can create names, addresses, text, URLs, random numbers, and other values using Faker-based functions, and can control how many related records are generated. Output can go to standard output, files, SQLAlchemy-accessible databases, or Salesforce when used through CumulusCI. File formats include JSON, TXT, CSV, SQL, DOT, SVG, SVGZ, JPEG, PostScript, and PNG; some diagram formats require Graphviz. Snowfakery can also be embedded in Python applications, and Python plugins and providers can extend its functions. Strict mode validates recipes before generation, while validate-only mode checks them without producing data. For database output, it creates new tables, with generated columns as text, and requires models to accommodate an ID field. It is free and open source, with a self-hosted installation path. Its documentation cautions that recipes can access much of Python's power, so untrusted recipes should not run where they can reach secure resources.
Good
- Creates related records with controlled quantities.
- Exports to JSON, CSV, SQL, and other formats.
- Can embed in Python applications.
- Validates recipes before generation.
- Free and open source.
Watch out
- Database output creates new tables only.
- Generated database columns are text.
- SQLite is the only SQL dataset URL in its test suite.
- Untrusted recipes can access substantial Python capabilities.
Who it suits
Snowfakery suits developers and data teams who need generated relational test data or file and database outputs, including Salesforce users working through CumulusCI. Users should be comfortable reviewing recipes and considering the access they grant them.
Verdict
Snowfakery offers flexible recipe-based synthetic data generation, with relational output and several integration paths. Its database table and column constraints, limited database testing, and recipe security cautions are important considerations before adopting it.
Plans and prices
From Snowfakery's own pricing page, checked 9 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| Snowfakery | Free | Open-source tool; no paid plans or usage limits stated |
The facts
Each one read from Snowfakery's own pages, with the date it was checked.
What it does
- Database integration
- Snowfakery can write to databases accessible through SQLAlchemy, including SQLite, and its documentation describes connecting to databases such as PostgreSQL, MySQL/MariaDB, and Oracle.checked 9 Oct 2026
- Database limitation
- For database output, Snowfakery currently creates new tables rather than importing data into existing tables, and generated columns are text.checked 9 Oct 2026
- Database support
- Snowfakery builds on SQLAlchemy connectors, but its documentation says only SQLite is included in its test suite for SQL dataset URLs.checked 9 Oct 2026
- Database testing
- Snowfakery is not proactively tested with every output database, and the documentation invites users to share their database experiences and report issues.checked 9 Oct 2026
- Data generation
- Recipes can generate fake names, addresses, text, URLs, and other values using Faker-based functions.checked 9 Oct 2026
- Extensibility
- Python plugins can add functions, and providers can add Faker capabilities to Snowfakery.checked 9 Oct 2026
- File formats
- File-based outputs include JSON, TXT, CSV, SQL, DOT, SVG, SVGZ, JPEG, and PS PNG.checked 9 Oct 2026
- Generated data
- Recipes can generate fake names, addresses, text, URLs, and other values, as well as random numbers.checked 9 Oct 2026
- ID requirement
- Snowfakery always generates an ID for each record and requires models that can accommodate an ID field.checked 9 Oct 2026
- Installation
- The documentation instructs non-Salesforce users to install Snowfakery with pipx and recommends installing CumulusCI for Salesforce use.checked 9 Oct 2026
- Intended users
- The documentation describes installation paths for Salesforce users and for users who want to use Snowfakery without Salesforce.checked 9 Oct 2026
- Output destinations
- It can write to standard output, databases accessible through SQLAlchemy, and Salesforce when used with CumulusCI.checked 9 Oct 2026
- Output formats
- File-based output includes JSON, TXT, CSV, SQL, DOT, SVG, SVGZ, JPEG, and PostScript formats, with some diagram formats requiring Graphviz.checked 9 Oct 2026
- Purpose
- Snowfakery generates unique, random fake data with relationships between tables from YAML recipe files.checked 9 Oct 2026
- Python use
- Snowfakery can be embedded in Python applications through its generate_data function.checked 9 Oct 2026
- Relationships
- Recipes can create related records and control how many related objects are generated for each record.checked 9 Oct 2026
- Repository status
- The Snowfakery GitHub repository was archived by its owner on September 18, 2026, and is read-only.checked 9 Oct 2026
- Salesforce integration
- When used through CumulusCI, Snowfakery can generate records in a Salesforce org.checked 9 Oct 2026
- Security
- The documentation warns that recipes can access much of Python's power, load plugins, and call Python methods, so it advises against running untrusted recipes where they can access secure resources.checked 9 Oct 2026
- Validation
- Strict mode validates a recipe before generation, and validate-only mode checks a recipe without generating data.checked 9 Oct 2026
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