Faker vs SDV vs Misata: Choosing the Right Python Synthetic Data Tool

Generating synthetic data in Python means picking from three very different tools: Faker, SDV, and Misata. Each solves a distinct problem. This page shows the difference with working code so you can pick the right one in under five minutes.

Quick comparison

Feature	Faker	SDV	Misata
Multi-table relational output	✗	✓ (limited)	✓
Referential integrity (FK)	Manual	✓	✓
Plain-English story → schema	✗	✗	✓
Exact aggregate targets (MRR, fraud rate)	✗	✗	✓
No real data required	✓	✗ (needs training data)	✓
Domain-realistic distributions	Partial	✓	✓
Database seeding (SQLAlchemy)	Manual	✗	✓
Outcome curves / scenario events	✗	✗	✓
LLM-powered schema generation	✗	✗	✓
Reproducible seed	✓	✓	✓
Install size	Small	Large (~1 GB w/ deps)	Medium

Faker — row-level fake data, no relationships

Faker is excellent for generating standalone fake values. It has hundreds of providers covering names, addresses, credit card numbers, and more.

from faker import Faker
fake = Faker()

# One row of user data — fast and simple
print(fake.name())         # "Patricia Mueller"
print(fake.email())        # "tricia23@example.net"
print(fake.credit_card_number())  # "4532015112830366"

Where Faker falls short:

# Building a customers → orders relationship by hand
customers = [{"id": i, "email": fake.email()} for i in range(1000)]

# You must manually wire FK integrity yourself — nothing enforces it
orders = [
    {
        "order_id": j,
        "customer_id": random.randint(0, 999),  # could reference a non-existent id
        "amount": round(random.uniform(10, 500), 2),
    }
    for j in range(5000)
]

There is no referential integrity, no distribution control, and no concept of business constraints. Every relationship you need, you build by hand.

Use Faker when: you need fake names, addresses, or values for a single table in a test fixture or form demo.

SDV (Synthetic Data Vault) — statistical models from real data

SDV learns statistical patterns from your actual data and generates synthetic rows that match those patterns. It handles single tables, conditional sampling, and some relational modeling.

from sdv.tabular import GaussianCopula

# SDV requires your real data to fit a model
model = GaussianCopula()
model.fit(real_customers_df)          # ← needs real data

synthetic = model.sample(num_rows=1000)

Where SDV falls short:

You must have real data to train from — no real data, no model.
Exact business rules ("fraud rate must be exactly 2%") cannot be pinned.
Install pulls in PyTorch, CUDA libs, and ~1 GB of dependencies.
Multi-table relational support is limited; deep FK chains require significant configuration.

Use SDV when: you have real data you cannot use directly (privacy concerns), and you want statistically faithful synthetic copies of it.

Misata — story-driven relational synthetic data

Misata generates multi-table datasets from plain-English descriptions. No real data required. Referential integrity is guaranteed. Business constraints (fraud rates, MRR targets, churn percentages) are pinned exactly.

import misata

# One line — no real data, no model training, no FK wiring
tables = misata.generate("A fintech company with 2000 customers and banking transactions.", seed=42)

customers    = tables["customers"]     # 2,000 rows
accounts     = tables["accounts"]      # ~4,000 rows
transactions = tables["transactions"]  # ~20,000 rows

# Referential integrity is automatic
assert (~transactions["account_id"].isin(accounts["account_id"])).sum() == 0

Exact aggregate pinning

schema = misata.parse(
    "A SaaS company with 1000 users. "
    "MRR rises from $50k in January to $200k in December with a dip in September.",
    rows=1000,
)
tables = misata.generate_from_schema(schema)

# Monthly MRR sums match the targets exactly — not approximately
monthly = tables["subscriptions"].groupby(
    pd.to_datetime(tables["subscriptions"]["start_date"]).dt.month
)["mrr"].sum()

# Jan → $50,000.00  ✓
# Dec → $200,000.00 ✓
# All 12 months hit their targets to the cent

Domain-realistic distributions out of the box

Misata ships calibrated priors for 7 domains. Healthcare blood-type frequencies match real ABO/Rh statistics. Credit scores match real FICO distributions. MRR follows a log-normal curve because real SaaS revenue is log-normal.

# Healthcare — blood types follow real ABO/Rh frequencies (no config needed)
tables = misata.generate("A hospital with 500 patients and doctors.", seed=42)
patients = tables["patients"]

# O+: 38% real-world → ~38% generated  ✓
# A+: 34% real-world → ~34% generated  ✓
print(patients["blood_type"].value_counts(normalize=True).mul(100).round(1))

Schema inspection before generation

schema = misata.parse("An ecommerce store with 10k orders")
print(schema.summary())
# Schema: EcommerceDataset
# Domain: ecommerce
# Tables (2)
#   customers  10,000 rows  [customer_id, email, name, signup_date]
#   orders     60,000 rows  [order_id, customer_id, order_date, amount, status]
# Relationships (1)
#   customers.customer_id → orders.customer_id

Use Misata when: you need relational multi-table data with business logic, exact aggregate targets, or domain-realistic distributions — and you don't want to write schema config by hand.

Side-by-side: the same task in each tool

Task: Generate 1,000 customers and 5,000 orders with referential integrity, where order amounts follow a realistic right-skewed distribution.

Faker

import random
from faker import Faker

fake   = Faker()
ids    = list(range(1, 1001))
valid  = set(ids)

customers = [{"customer_id": i, "email": fake.email(), "name": fake.name()} for i in ids]

# Manual FK wiring — you're responsible for this
orders = [
    {
        "order_id":    j,
        "customer_id": random.choice(ids),             # could be any id
        "amount":      round(random.lognormvariate(4, 0.8), 2),  # lognormal by hand
        "order_date":  fake.date_between("-2y", "today"),
    }
    for j in range(1, 5001)
]

# No integrity guarantee — you'd need to assert this yourself

~30 lines. You wrote the distribution, the FK logic, and the date range manually.

SDV

# SDV requires real data — this task cannot be completed from scratch.
# You would need real customer and order tables to fit the model first.

Not applicable without a real dataset to train from.

Misata

import misata

tables    = misata.generate("An ecommerce store with 1000 customers and orders.", seed=42)
customers = tables["customers"]   # 1,000 rows, realistic email/name/signup_date
orders    = tables["orders"]      # 5,000+ rows, right-skewed amounts

# FK integrity is guaranteed — no assertion needed, but it holds:
assert (~orders["customer_id"].isin(customers["customer_id"])).sum() == 0

3 lines. Referential integrity, realistic distributions, and reproducibility included.

When to use each

You want to…	Use
Generate fake names/emails/addresses for a form or test fixture	Faker
Create a privacy-safe copy of your production database	SDV
Build a realistic multi-table dataset from scratch (no real data)	Misata
Seed a test database with consistent relational data	Misata
Pin exact KPIs (fraud rate, churn %, MRR targets) in generated data	Misata
Generate data for BI demos, load testing, or ML training	Misata
Learn distributions from existing data	SDV
One-off fake value in a script	Faker

Installation

# Faker
pip install faker

# SDV (large, requires Python 3.8–3.11)
pip install sdv

# Misata (no real data required)
pip install misata

# Misata with LLM-powered schema generation
pip install "misata[llm]"