PySynthData: Synthetic World Generation Platform

Generate complete synthetic worlds from schemas, data, or natural language. Production-ready with compliance, governance, and enterprise features.

Status: ✅ Production Ready (v0.2.0) | LOC: 4,900+ | Modules: 16 | Python: 3.10+

What It Does

Generate realistic synthetic worlds for:

• 🎯 AI training data (GDPR/HIPAA compliant)
• 🤖 Robotics fleet simulation (100+ robots, ROS2-native)
• 🧪 Data pipeline stress testing (5 calibrated chaos levels)
• 📊 Enterprise digital twins
• 🔬 Reproducible research datasets

Core Features

✅ Schema → Synthetic Data: YAML/JSON schemas generate realistic records with referential integrity
✅ Temporal Behaviors: Entity state machines, event sequences, edge case synthesis
✅ Robotics Ready: Fleet coordination, collision detection, sensor simulation, ROS2 integration
✅ Domain Intelligence: 5 pre-configured domains + custom domain support
✅ Production Chaos: 35+ realistic data quality patterns (missing values, encoding errors, cascading failures)
✅ Enterprise Features: Compliance frameworks (GDPR/HIPAA/SOC2), audit logging, lineage tracking, regulatory reporting
✅ Quality Monitoring: Drift detection, anomaly detection, performance tracking
✅ Cost Transparent: Automatic cost estimation and performance profiling

Quick Start

Install

pip install pysynthdata

Generate Synthetic Banking World

from pysynthdata import WorldGenerator, DomainKnowledgeBase

# Use pre-configured Banking domain
kb = DomainKnowledgeBase()
schema = kb.get_domain("Banking").schema

# Generate 1M synthetic customers, accounts, transactions
gen = WorldGenerator(schema)
world = gen.generate(num_records=1_000_000, seed=42)

# Export to Parquet
world.to_parquet("synthetic_banking/")

Add Realistic Messiness

from pysynthdata import RealWorldMessGenerator, MessinessLevel

# Generate data that looks like production
data = generate_base_data()
gen = RealWorldMessGenerator(seed=42)
gen.apply_level(data, MessinessLevel.LEVEL_3)  # 15-40% affected

# Test if your pipeline survives
try:
    result = pipeline.process(data)
    print("✅ Pipeline handles Level 3 chaos")
except Exception as e:
    print(f"❌ Pipeline fails: {e}")

Monitor Quality & Drift

from pysynthdata import DriftDetector, AnomalyDetector, MonitoringConfig

# Enable comprehensive monitoring
config = MonitoringConfig(
    track_schema_drift=True,
    track_data_drift=True,
    track_constraint_violations=True,
    track_edge_cases=True
)

# Generate compliance report
gdpr_report = world.get_compliance_report(ComplianceFramework.GDPR)
print(f"GDPR Status: {gdpr_report.compliance_status}")

Simulate Robot Fleet

from pysynthdata import FleetSimulation, Environment, Robot

env = Environment(name="Warehouse", type="warehouse", width=100, height=100)
sim = FleetSimulation(env)

# Add 100 robots
for i in range(100):
    robot = Robot(id=f"robot_{i}", type="mobile_base", battery=100)
    sim.add_robot(robot)

# Simulate operations and generate ROS2 sensor streams
simulation = sim.run_simulation(duration_hours=1_000_000)

5 Calibrated Messiness Levels

Test your pipeline at increasing chaos levels:

• Level 1: Slightly Messy (2-5% affected) — MVP testing
• Level 2: Moderately Messy (5-15% affected) — Early production
• Level 3: Very Messy (15-40% affected) — Real production data
• Level 4: Extremely Messy (40-70% affected) — Post-incident recovery
• Level 5: Nightmare Mode (70%+ affected) — Breaking point testing

for level in [MessinessLevel.LEVEL_1, MessinessLevel.LEVEL_3, MessinessLevel.LEVEL_5]:
    data = generate_synthetic_world()
    gen.apply_level(data, level)
    
    try:
        result = pipeline.process(data)
        print(f"{level}: PASS")
    except Exception as e:
        print(f"{level}: FAIL - {e}")

Enterprise Features

Compliance Ready

• ✅ GDPR: Auto-anonymization, consent tracking, audit trail
• ✅ HIPAA: PHI encryption, access controls, breach notification
• ✅ SOC2: Change management, monitoring, incident response
• ✅ Custom: Extend to any compliance framework

Data Governance

• Lineage tracking (source → transformations → audit)
• Policy enforcement (access control, retention)
• Reproducibility guarantees (fixed seed = exact regeneration)
• Cost estimation (per operation, per record)

Production Data Patterns (35+)

• Legacy system cruft (mixed ID formats, NULL chaos)
• Human errors (typos, field swaps, copy-paste truncation)
• System failures (encoding mismatches, timeouts, cascading errors)
• Temporal chaos (timezone confusion, Y2K bugs, daylight savings)
• Migration artifacts (partial loads, schema mixing, broken FKs)

Documentation

• Quick Start — 5-minute examples
• Messiness Levels — Calibrated chaos for testing
• Enterprise Features — Compliance, governance, audit
• Phase 2-4 Guide — Architecture deep-dive
• Complete Platform — Full feature inventory

Building & Development

# Build Rust core
cargo build --release

# Build Python wheels
pip install maturin
maturin develop              # Development install
maturin build --release      # Production wheel

# Run tests
cargo test
pytest tests/

Supported Domains

Performance

License

Proprietary - All rights reserved. Contact mullassery@gmail.com for licensing inquiries.

Contributing

PRs welcome for:

• New domains & patterns
• Performance optimizations
• Additional compliance frameworks
• Documentation & examples
• Bug reports & feature requests

Citation

If you use PySynthData in research, please cite:

@software{pysynthdata2026,
  author = {Mullassery, Georgi},
  title = {PySynthData: Synthetic World Generation Platform},
  year = {2026},
  url = {https://github.com/Mullassery/PySynthData}
}

Questions? Open an issue on GitHub or email mullassery@gmail.com