snowfakery

library4.2.1pypypi✓ verified 90d ago

Snowfakery is a Python tool for generating fake data models with relational integrity. It allows users to define complex data structures, including relationships between 'tables' (objects), using YAML configuration. Each generated record is unique and random. Currently at version 4.2.1, it maintains an active development pace with frequent minor releases and occasional major updates addressing Python compatibility and core feature enhancements.

data testing database

pip install snowfakery

github ↗package ↗homepage ↗

INSTALL

IMPORT

SIG · SNOWFAKERY

snowfakery

datapythonv4.2.1

Install

7.9s avg

Import

1794ms

Disk

88MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v3.6.3 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.910 runs

installs and imports cleanly · install 0.0s · import 1.867s · 84.1MB

glibc

py 3.10–3.910 runs

installs and imports cleanly · install 7.9s · import 1.721s · 82MB

88MB installed

● package 88MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

generate_data

✓ from snowfakery.api import generate_data

✗ from snowfakery import generate_data

The primary programmatic interface `generate_data` is located within the `snowfakery.api` submodule, not directly under `snowfakery`.

This quickstart demonstrates how to programmatically use Snowfakery to generate JSON data based on a YAML data model. It uses `io.StringIO` to avoid creating temporary files, making it suitable for in-memory execution.

import io from snowfakery.api import generate_data # Define a simple Snowfakery data model in YAML data_model_yaml = """ - object: Contact count: 2 fields: FirstName: random_first_name: {} LastName: random_last_name: {} Email: formula: f"{FirstName}.{LastName}@example.com" """ # Use io.StringIO to simulate file input and output for a quick example input_stream = io.StringIO(data_model_yaml) output_stream = io.StringIO() # Generate data generate_data( yaml_file=input_stream, output_format="json", # or "csv", "sqlite", "sql", "db" output_file=output_stream ) # Print the generated JSON data print(output_stream.getvalue())

snowfakery --version

Debug

Known issues

breakingSnowfakery v4.0.0 dropped support for Python versions 3.8, 3.9, and 3.10. Users must upgrade to Python 3.11 or newer to use Snowfakery 4.x.x.

fix

Upgrade your Python environment to version 3.11 or later. Alternatively, stick to Snowfakery versions < 4.0.0 if you must use an older Python version (e.g., `pip install 'snowfakery<4'`).

affects: >=4.0.0

breakingWith Snowfakery v3.0.0 and the `snowfakery_version: 3` declaration in YAML, formula outputs can be types other than strings. Previously, all formula outputs were implicitly converted to strings. This change affects how formulas interact with fields expecting non-string types.

fix

Ensure your formulas explicitly return the desired type if you rely on specific non-string outputs. For compatibility with older behaviors, remove `snowfakery_version: 3` from your YAML or adjust formulas to explicitly cast to strings if needed.

affects: >=3.0.0

gotchaSnowfakery's core functionality relies heavily on its custom YAML schema. Incorrect indentation, misspelled keywords, or invalid generator/function usage are common sources of errors.

fix

Always refer to the official Snowfakery documentation for correct YAML syntax. Use an IDE with YAML linting, and consider using the provided JSON Schema for validation if available (`snowfakery --json-schema`).

affects: all

gotchaFeatures like `find_record` and SObject Upserts/Updates are specifically designed for integration with Salesforce and often require the CumulusCI framework or specific Salesforce connection setup. They will not work out-of-the-box in a generic Python environment.

fix

Only use Salesforce-specific features when integrated with a Salesforce project and CumulusCI, or ensure your environment is configured with the necessary Salesforce connection details.

affects: all

Errors

Common errors & fixes

ERROR: Package 'snowfakery' requires a different Python version: 3.11+ but you have 3.10.x

Attempting to install or run Snowfakery v4.0.0+ on an unsupported Python version (3.10 or older).

fix

Upgrade your Python environment to 3.11 or newer, or install an older version of Snowfakery: `pip install 'snowfakery<4'`.

yaml.scanner.ScannerError: while scanning a simple key

Incorrect YAML syntax, often due to improper indentation, missing colons, or invalid key structures.

fix

Carefully review your Snowfakery YAML definition for syntax errors. Pay close attention to indentation and ensure all keys are correctly terminated with colons. Use a YAML linter.

NameError: name 'my_custom_generator' is not defined

A custom generator or function is referenced in the YAML but has not been correctly registered or is not in scope.

fix

Ensure that any custom generators or functions are correctly defined and passed to `generate_data` using the `extra_functions` or `extra_generators` parameters if used programmatically, or defined in an included file if using the CLI.

Upgrade

Version history

4.2.1latest on PyPI · released Jan 9, 2026

Audit

Dependencies

pythonrequiredSnowfakery 4.x.x requires Python 3.11 or newer.

Agent activity

12 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/SFDO-Tooling/Snowfakery ↗

packagehttps://pypi.org/project/snowfakery/ ↗

homepagehttps://snowfakery.readthedocs.io ↗