datacontract-faker

datacontract-faker reads an ODCS v3.1.0 data contract and produces realistic, contract-compliant synthetic data — as a CLI tool or a Python library.

datacontract-faker generate orders.yaml -r 50000 -o data/orders.parquet -f parquet

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Highlights

• Strict contract compliance — minimum, maximum, exclusiveMinimum, exclusiveMaximum, multipleOf, minLength, maxLength, pattern, examples, format, and length-constrained types are all enforced on every row.
• Foreign-key integrity — declares from relationships: are honored across models. Referenced tables are generated first; child rows sample real parent keys.
• Nested + array types — recursive object fields and array items (including arrays of objects) generate properly typed values.
• Reproducible — pin --seed for byte-identical output across runs.
• Four output formats — JSON, JSONL, CSV, Parquet (via polars + pyarrow).
• Locale-aware — any Faker locale (en_US, de_DE, ja_JP, …).
• Library-first — every component (ContractParser, SyntheticGenerator, ProviderMapper, Exporter) is independently importable.

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Install

pip install datacontract-faker          # standard
pipx install datacontract-faker         # isolated CLI
poetry add datacontract-faker           # poetry projects

Requires Python ≥ 3.11.

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CLI

generate

Generate synthetic data from a contract.

datacontract-faker generate CONTRACT [OPTIONS]

Option	Short	Default	Description
CONTRACT	—	required	Path to the ODCS contract (YAML), as a positional argument
--rows INT	-r	100	Rows to generate per model
--output PATH	-o	—	Output file. Omit to preview in the terminal
--format	-f	json	csv | json | jsonl | parquet
--model TEXT	-m	all	Generate only this model
--locale TEXT	—	en_US	Faker locale
--seed INT	—	—	Seed for reproducible output
--nullable-ratio FLOAT	—	0.1	Probability of null for optional fields (0.0–1.0)
--validate-only	—	false	Only validate the contract; skip generation
--verbose	-v	false	Debug logging to stderr

When a contract has multiple models and --output is set, each model is written to {stem}_{model}{suffix} — e.g. data.parquet becomes data_orders.parquet, data_customers.parquet.

inspect

Render the parsed schema as a table — type, format, flags (PK, req, uniq, →fk_target), examples, and constraints.

datacontract-faker inspect CONTRACT [--nullable-ratio FLOAT]

Recipes

# Preview 10 rows in the terminal
datacontract-faker generate contract.yaml -r 10

# Validate without generating
datacontract-faker generate contract.yaml --validate-only

# 1M rows to Parquet, deterministic
datacontract-faker generate contract.yaml -r 1000000 -o out.parquet -f parquet --seed 42

# Single model, German locale, NDJSON
datacontract-faker generate contract.yaml -m customers --locale de_DE -o cust.jsonl -f jsonl

# Stress-test null handling
datacontract-faker generate contract.yaml --nullable-ratio 0.5

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Python API

Every CLI capability is available as a library. Public exports from the top-level package:

from datacontract_faker import (
    ContractParser, ContractValidationError,
    SyntheticGenerator,
    Exporter, OutputFormat,
    FieldSpec, GenerationSchema, QualityRule,
)

End-to-end

from pathlib import Path
from datacontract_faker import ContractParser, SyntheticGenerator, Exporter, OutputFormat

schema = ContractParser(nullable_ratio=0.05).load_and_validate(Path("contract.yaml"))

gen = SyntheticGenerator(schema, rows=10_000, seed=42, locale="en_US")
dataframes = gen.generate_all()   # dict[str, polars.DataFrame], FK-aware order

exporter = Exporter()
for name, df in dataframes.items():
    exporter.export(df, Path(f"output/{name}.parquet"), OutputFormat.PARQUET)

generate_all() honors foreign-key relationships: referenced models are generated first and child models sample real parent keys.

Generate a single model

df = gen.generate_model("orders")          # polars.DataFrame
df = gen.generate_model("orders", fk_pools={"customer_id": ["c1", "c2", "c3"]})

Parse without file I/O

schema = ContractParser().parse_string(contract_yaml_str)

Custom Faker providers

Override or extend the type-to-provider resolution:

from datacontract_faker.mapper import ProviderMapper
from datacontract_faker import SyntheticGenerator

mapper = ProviderMapper(
    logical_format_overrides={
        "string:product_sku": lambda f: f.bothify("???-####").upper(),
    },
    logical_overrides={
        "integer": lambda f: f.random_int(min=1000, max=9999),
    },
    physical_overrides={
        "decimal": lambda f: round(f.pyfloat(min_value=1, max_value=500), 2),
    },
)

gen = SyntheticGenerator(schema, rows=500, mapper=mapper)

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Output formats

Format	Flag	Extension	Notes
JSON (array)	json	.json	Pretty-printed, UTF-8
NDJSON	jsonl	.jsonl	One record per line, stream-friendly
CSV	csv	.csv	UTF-8, no index
Apache Parquet	parquet	.parquet	Columnar, compressed (recommended for large datasets)

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Stack

datacontract-cli for contract validation · Faker for synthetic values · polars + pyarrow for DataFrames and Parquet · rstr for regex-conforming strings · Typer + Rich for the CLI.

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License

MIT