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Misata — Proof-Backed Python Synthetic Data Generator

Realistic multi-table synthetic data with validation reports — from a sentence, YAML, or your own database.

No ML model. No real data needed. Referential integrity guaranteed across every table. Every CLI generation writes an Oracle report by default.

misata generate \
  --story "Brazilian fintech with R$ payments, CPF verification, and 3% fraud" \
  --rows 1000 \
  --output-dir ./demo_data

# CSVs + ./demo_data/oracle_report.json

The Oracle report separates hard guarantees from advisory realism checks: row counts, referential integrity, constraints, temporal consistency, locale/domain fit, privacy notes, fidelity scores, and reproducibility metadata.

Get started → View on GitHub →

import misata

tables = misata.generate(
    "A SaaS company with 5k users, monthly subscriptions, and 20% churn"
)
print(tables["users"].head())
print(tables["subscriptions"].head())

# misata.yaml
name: my-app
seed: 42

tables:
  users:
    rows: 1000
    columns:
      user_id: { type: int, unique: true }
      email:   { type: text, text_type: email }
      plan:    { type: categorical, choices: [free, pro, enterprise] }
  orders:
    rows: 5000
    columns:
      order_id: { type: int, unique: true }
      user_id:  { type: foreign_key }
      amount:   { type: float, min: 5.0, max: 500.0 }

relationships:
  - "users.user_id → orders.user_id"

schema = misata.load_yaml_schema("misata.yaml")
tables = misata.generate_from_schema(schema)

from misata import schema_from_db, generate_from_schema, seed_database

schema = schema_from_db("postgresql://user:pass@localhost/myapp")
tables = generate_from_schema(schema)
report = seed_database(tables, "postgresql://user:pass@localhost/myapp_dev")
# SeedReport: seeded 6 tables, 47,300 rows in 1.2s

Install

pip install misata

Why Misata over Faker or SDV?

Faker generates one column at a time — you write the loop, manage the IDs, enforce uniqueness yourself. SDV requires real training data and a fitted model before you get anything. Misata generates a complete relational dataset from a single sentence — no training data, no boilerplate.

Feature Faker SDV Misata
One-liner multi-table generation — — ✓
Story auto-detects locale + country stats — — ✓
18 built-in domain schemas (SaaS → streaming) — — ✓
Narrative growth curves (Q4 push, Black Friday, 10×) — — ✓
MCP server — usable from Claude / Cursor / Windsurf — — ✓
YAML schema committed to git — — ✓
DB introspection → generate → re-seed — Limited ✓
Direct PostgreSQL / SQLite seeding — — ✓
FK integrity across all tables — ✓ ✓
Fully offline, no LLM required ✓ ✓ ✓
Time-series generation — — ✓
15 country locale packs — — ✓
Shareable Oracle report — — ✓

Narrative control — tell Misata exactly what shape your data should have

Misata understands natural-language growth patterns and turns them into exact monthly targets:

# Seasonal ecommerce with named events
misata.generate("Ecommerce store with 5k orders — Black Friday spike, Christmas peak")

# SaaS growth curve with a quarterly dip
misata.generate("SaaS mrr from $50k in Jan to $200k in Dec, Q3 slump")

# 10× startup trajectory
misata.generate("SaaS startup — MRR 10x growth over the year")

# Quarterly narrative
misata.generate("Fintech payments — strong Q4, dip in Q1, flat Q2")

Quarterly patterns (Q1–Q4), named events (Black Friday, Christmas, summer slump, back to school), multipliers (doubled, tripled, 10x, halved) — all resolve to exact per-month control points in the generated data.

Use Misata from Claude, Cursor, or Windsurf (MCP)

Misata ships a built-in Model Context Protocol server. Once configured, AI assistants can generate synthetic data on the user's behalf from natural language.

pip install "misata[mcp]"

Add to Claude Desktop config:

{ "mcpServers": { "misata": { "command": "misata-mcp" } } }

Then ask Claude: "Generate a fintech fraud dataset with 1 000 customers and a 2% fraud rate."

MCP setup guide →

What people use Misata for

  • Test data generation — seed your dev/staging database with realistic data in seconds
  • BI and dashboard demos — populate a demo environment with coherent, believable numbers
  • Database seeding in CI/CD — reproducible, seed-controlled datasets for every test run
  • Privacy-safe development — work with realistic schemas without exposure to PII
  • AI agent workflows — let Claude or Cursor generate the dataset directly via MCP

Supported domains

Misata auto-detects your domain from the story and applies statistically accurate priors:

saas · ecommerce · fintech · healthcare · marketplace · logistics · hr · social · realestate · pharma · fooddelivery · edtech · gaming · crm · crypto · insurance · travel · streaming

See all domains →