PySynthData

A Rust-native library for generating synthetic relational datasets from a schema: real row generation with foreign keys, constraints, differential privacy support, and quality scoring.

What it is

PySynthData generates realistic synthetic relational data from a declarative schema. It ensures foreign keys point to valid parents, enforces constraints, scores data quality, and exports to common formats. Core generation and quality scoring run in Rust with a Python API.

Features / Highlights

• Schema-driven generation: Define entities, fields, relationships, and constraints (YAML or Python API).
• Relational integrity: Foreign keys are populated from existing parent rows in dependency order.
• Constraint enforcement: Supports nullability, uniqueness, ranges, lengths, patterns, and enums.
• Differential privacy: Laplace mechanism noise injection with a real ε-δ budget that prevents overspending.
• Streaming generation: Generate large datasets in chunks to bound peak memory.
• Quality scoring: Computes fidelity based on actual violations (not hardcoded).
• Export formats: Pandas DataFrames, Parquet, and JSON.
• Schema inference: Infer schemas from live Postgres, MySQL, or SQLite databases via SQLAlchemy.

Who it's for / Use cases

• Testing and demos: Generate realistic relational fixtures without sharing production data.
• Privacy-safe development: Create synthetic derivatives of sensitive data with differential privacy.
• Database prototyping: Populate schemas with consistent relational data for development.
• CI/testing: Produce deterministic, reproducible test datasets from versioned schemas.

What it is not

• Not a full SDV replacement for all use cases: Focused on relational schema fidelity, constraints, and privacy primitives; advanced ML-based synthesis is out of scope.
• Not compliance certification: Differential privacy is a technical primitive, not a guarantee of regulatory compliance.
• Alpha maturity: Some bundled components exist in Rust but are not exposed through the Python API (see roadmap).

Project status

Version 0.5.0 (published to PyPI). Core pipeline (schema definition, row generation, FKs, constraints, quality scoring, exports, differential privacy, streaming, schema inference) is implemented and tested. See ROADMAP_HONEST.md for unexposed work and limitations.

Installation

pip install pysynthdata

With database schema inference support:

pip install pysynthdata[db]

Quick start

from pysynthdata import Schema, WorldGenerator

schema = Schema()
schema.add_entity("customers")
schema.add_field("customers", "id", "uuid", unique=True)
schema.add_field("customers", "name", "string")
schema.add_field("customers", "age", "int")
schema.add_field("customers", "status", "enum(active,suspended,closed)")
schema.add_constraint("range", "customers", "18-90", field="age")

schema.add_entity("orders")
schema.add_field("orders", "id", "uuid", unique=True)
schema.add_field("orders", "customer_id", "uuid")
schema.add_relationship("customers", "orders", "id", "customer_id", "1:n")

generator = WorldGenerator(schema)
world = generator.generate(num_records=1000, seed=42)

df = world.to_pandas("customers")
world.to_parquet("out/")
world.to_json("out/world.json")

print(world.fidelity_score)
print(world.quality_report)

Generation is deterministic: same schema + seed produces identical results. Foreign keys always reference valid parent rows.

Usage

From YAML

generator = WorldGenerator.from_yaml("examples/banking_schema.yaml")
world = generator.generate(num_records=5000, seed=7)

Schema inference from database

pysynthdata infer-schema --db-url "postgresql+psycopg://user:pass@host/dbname" \
    --table customers --output customers_schema.yaml

pysynthdata infer-schema --db-url "mysql+pymysql://user:pass@host/dbname" \
    --all-tables --output full_schema.yaml

Drivers (psycopg/psycopg2, pymysql) must be installed separately. Output YAML is compatible with Schema.from_yaml()/WorldGenerator.from_yaml().

Differential privacy

from pysynthdata import PrivacyBudget

budget = PrivacyBudget(epsilon=1.0, delta=1e-5)
private_world = generator.generate_private(num_records=1000, seed=42, budget=budget, epsilon=0.3)
print(private_world.privacy_report)
print(budget.remaining_epsilon)

Streaming generation

for entity_name, rows in generator.generate_streaming(num_records=5_000_000, seed=1, chunk_size=50_000):
    ...

generator.to_parquet_streaming(num_records=5_000_000, seed=1, out_dir="out/", chunk_size=50_000)

Data quality analysis

from pysynthdata import DataQualityAnalyzer

analyzer = DataQualityAnalyzer()
df = world.to_pandas("customers")
report = analyzer.analyze(df, entity_name="customers")
print(report)

Documentation

• Examples
• Roadmap & honest limitations
• Contributing
• Security policy
• Code of conduct

Related projects

Part of the Mullassery open-source family:

• PyDependencyCheck — dependency intelligence and supply-chain integrity
• PyDBTGuard — dbt manifest analysis (risky tests, blast radius, cost)
• PyAirflowTester — Airflow and dbt reliability platform
• StatGuardian — declarative data-quality framework
• PySynthData — synthetic data generation for ML
• PyReverseETL — quality-validated reverse ETL
• PyWeatherEnriched — weather-enriched feature engineering
• Browse all projects

License

Licensed under the Apache License, Version 2.0. See LICENSE for details.