Stop writing fake data scripts.
Generate production-grade datasets from natural language.
Quick Start β’ Features β’ Python API β’ Enterprise
Misata isn't just a random data generator. It's an intelligent engine that understands your business logic, relationships, and constraints. Whether you need 50 rows for unit tests or 10 million rows for load testing, Misata delivers statistically realistic data that looks and behaves like the real thing.
| Feature | Faker | SDV | Misata |
|---|---|---|---|
| Natural Language Input | β | β | β |
| Auto Schema Generation | β | β | β |
| Relational Integrity | β | β | β |
| Business Constraints | β | β | β |
| No Training Data Needed | β | β | β |
| Streaming (10M+ rows) | β | β | β |
pip install misataDescribe what you need in plain English. Misata handles the rest.
# Basic generation (Rule-based, instant)
misata generate --story "A SaaS platform with 50K users, monthly subscriptions, and a 20% churn rate in Q3"
# Intelligent generation (LLM-powered)
export GROQ_API_KEY=gsk_...
misata generate --story "E-commerce store with seasonal trends and customer segments" --use-llmMisata creates a relational schema, generates the data, and saves it to ./generated_data.
π Schema: SaaS_Platform
Tables: 4 (users, subscriptions, payments, events)
Relationships: 3
Events: 1 (Churn Spike Q3)
π Performance: 385,000 rows/second
πΎ Data saved to: ./generated_data
Already have a database? Misata can reverse-engineer your schema and seed it with realistic data.
# 1. Introspect your existing DB
misata schema --db-url postgresql://user:pass@localhost:5432/mydb --output schema.yaml
# 2. Seed it with 100K rows of realistic data
misata generate --config schema.yaml --db-url postgresql://... --db-truncateDescribe a chart, and Misata generates the underlying data to match it.
misata graph "Monthly revenue growing from $10k to $1M over 2 years, with a dip in August"Seamlessly integrate Misata into your test suites and CI/CD pipelines.
from misata import DataSimulator
from misata.llm_parser import LLMSchemaGenerator
# 1. Design schema with AI
llm = LLMSchemaGenerator(provider="groq")
config = llm.generate_from_story(
"Healthcare app with patients, doctors, and appointments"
)
# 2. Generate data
simulator = DataSimulator(config)
for table_name, df in simulator.generate_all():
print(f"Generated {len(df)} rows for {table_name}")
df.to_csv(f"{table_name}.csv", index=False)Directly seed your SQLAlchemy models without writing factories.
from misata import seed_from_sqlalchemy_models
from myapp.models import Base, engine
# Automatically analyzes your models and foreign keys
report = seed_from_sqlalchemy_models(
engine,
Base,
default_rows=10_000,
create=True,
smart_mode=True # Infers realistic values from column names
)
print(f"Seeded {report.total_rows} rows in {report.duration_seconds}s")Define complex rules that simple random generators can't handle.
from misata import Constraint, Table
timesheets = Table(
name="timesheets",
row_count=10000,
constraints=[
Constraint(
name="max_daily_hours",
type="sum_limit",
group_by=["employee_id", "date"],
column="hours",
value=8.0,
action="redistribute" # Automatically fixes violations
)
]
)Misata supports multiple LLM providers for schema generation.
| Provider | Env Var | Tier | Best For |
|---|---|---|---|
| Groq | GROQ_API_KEY |
Free | Speed (Recommended) |
| OpenAI | OPENAI_API_KEY |
Paid | Quality |
| Ollama | None | Free | Privacy (Local) |
Building a platform? Misata Studio is our commercial offering for teams.
- π₯οΈ Visual Schema Editor: Drag-and-drop schema design.
- π Privacy Filters: PII scanning and masking.
- π¦ One-Click Deploy: Docker & Kubernetes ready.
- π€ Support: Dedicated support and custom integration.
Contact Sales for a demo.