This repository contains the DATAMIMIC Community Edition (CE). MIT-licensed, Python-native, MCP-ready.

CE is fully usable standalone for deterministic synthetic data generation and PII-aware pseudonymization. The Enterprise Platform adds governed workflows, PII scanning, role-based access, audit logging, scheduling, multi-system execution, and the full operational layer that regulated enterprises require.

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DATAMIMIC CE is the open-source deterministic data engine at the core of the DATAMIMIC Enterprise Platform. It is usable standalone for synthetic data generation and PII-aware pseudonymization in any local, CI, or agent-driven workflow.

The Enterprise Platform adds the governed workflows, scanners, dashboards, and execution layer that regulated enterprises require for production-scale test-data operations.

Available in CE (this repo):

• Generate fully synthetic, deterministic datasets — model-driven, no source data required
• Pseudonymize staging/QA exports — deterministic (seeded) or privacy-maximized (non-seeded) field transformation; PII fields identified and modeled manually in the XML pipeline
• Execute single-system pipelines against PostgreSQL · MySQL · Oracle · MS SQL · SQLite · MongoDB · CSV · JSON · XML
• Emit provenance — append-only execution logs and per-output content hash for audit re-execution
• Serve agents — bundled MCP server exposing generate as a deterministic tool for AI/LLM tooling

The Enterprise Platform adds:

• PII scanner — probability-scored field detection with configurable thresholds via DataWorkbench
• Multi-system execution — Oracle / MongoDB / Kafka in coordinated workflows with referential integrity
• Industry message templates — EDIFACT / SWIFT MT / HL7 v2.x / HL7 FHIR generated as deterministic test/training artefacts
• Governance layer — role-based dashboards, audit trails, approval flows, reusable enterprise templates, scheduler
• Performance core — Rust fastpath, ML/auto-regressive engine for complex distributions, keyset and manifest building, optimised distributed execution
• On-premise / air-gapped deployment — podman-compose or Helm, with consulting-led rollout

Deployed in regulated EU banking environments for deterministic test data across Oracle, MongoDB, and Kafka pipelines. Reference customers available under NDA — see also datamimic.io case studies.

CE and EE are not the same engine with a feature flag. They share the DSL and determinism contract, but EE is an independently optimised execution engine built for enterprise-scale throughput and operational control.

👉 Explore the Enterprise Platform  |  Book a platform demo

The EE core supports three runtime configuration profiles, selectable per execution context:

Logging depth is independently configurable per profile — from minimal (throughput-optimised) to full nested tracing across importers, exporters, and generation stages.

The EE template engine generates industry-standard financial messages from DATAMIMIC models. The workbench parses uploaded message samples, auto-detects the message type, and validates edits against the registered spec version in real time.

• Spec-aware form editing — segments and elements rendered as structured forms with mandatory/optional indicators, per-field value suggestions, and inline custom-extension support
• Strict validation against baked spec versions, with segment- and element-level error reporting
• Advisory mode when a spec is unregistered or in draft — editing stays enabled, validation continues as guidance
• Round-trip between the structured form view and the authoritative template text — no fidelity loss
• Download / adjust / upload your own spec — customers can extend or override the baked spec catalogue without waiting for a release
• Live structure tree + preview for every edit
• File auto-detection — upload an existing message, the editor identifies the type and loads the matching spec

Customers can extend the spec catalogue between releases by downloading, adjusting, and uploading their own spec files directly.

Generated messages are deterministic and traceable to their source model, and syntactically valid against the registered spec. They are intended for test and training environments only — they are not network-validated and must not be transmitted on production SWIFTNet or EDI networks. See the SWIFT CSP note below.

Developers and data engineers who need deterministic synthetic data generation or PII-aware pseudonymization in local environments, CI pipelines, or agent-driven workflows. PII field identification is manual — the EE DataWorkbench automates this step.

Most test data tools produce random output. That breaks regression tests, audit trails, and cross-team reproducibility.

DATAMIMIC's determinism contract (CE):

• Same seed + same model = byte-identical output, every run, every machine. Holds at three layers: the generate_domain facade, every domain service called directly, and every literal generator that accepts an rng= argument. Verified per-service on every CI run via tests_ce/architecture/test_service_replay_determinism.py.
• Provenance hash on every facade output = re-executable lineage. Same input → same determinism_proof.content_hash, always.
• UUIDv5 entity identifiers = stable across runs and machines.
• Single wall-clock SPOT (now_utc_naive()); raw datetime.now() is forbidden in production code and the clock-drift architecture gate fails CI on any reintroduction.
• RNG/clock runtime SPOTs in datamimic_ce/domains/domain_core/runtime/: resolve_rng, spawn_rng, derive_child_seed, resolve_clock. Mirrors EE's ADR-030 / ADR-031 contract vocabulary.

from datamimic_ce.domains.facade import generate_domain

request = {
    "domain": "person",
    "version": "v1",
    "count": 1,
    "seed": "regression-suite-42",       # identical seed → identical output
    "locale": "en_US",
    "clock": "2025-01-01T00:00:00Z"      # fixed clock = stable time context
}

response = generate_domain(request)
# response["determinism_proof"]["content_hash"] is stable across runs.

Direct service use is equally deterministic when given a seeded RNG:

import random
from datamimic_ce.domains.finance.services import CreditCardService

# Same seeded Random → byte-identical CreditCard across runs.
card_a = CreditCardService(rng=random.Random(42)).generate()
card_b = CreditCardService(rng=random.Random(42)).generate()
assert card_a.bic == card_b.bic and card_a.card_number == card_b.card_number

CE delivers contract-enforced determinism for the synthetic-data generation surface (facade, services, generators). The Enterprise Platform extends the same contract across the full pipeline — custom XML descriptors, multi-system writes with referential integrity, the seeded/unseeded pseudonymization modes — and adds the five drift-gates that lock the contract end-to-end for regulated deployments.

# Faker — broken relationships
from faker import Faker
fake = Faker()
patient_age = fake.random_int(1, 99)
conditions  = [fake.word()]
# "25-year-old with Alzheimer's" — meaningless for any real test

# DATAMIMIC — domain-aware, deterministic
from datamimic_ce.domains.healthcare.services import PatientService
patient = PatientService().generate()
print(f"{patient.full_name}, {patient.age}, {patient.conditions}")
# "Shirley Thompson, 72, ['Diabetes', 'Hypertension']" — every time

pip install datamimic-ce

from datamimic_ce.domains.healthcare.services import PatientService

patient = PatientService().generate()
print(patient.full_name, patient.age, patient.conditions)
# Age-appropriate conditions, demographically realistic, deterministic

from datamimic_ce.domains.finance.services import BankAccountService

account = BankAccountService().generate()
print(account.account_number, account.balance)
# Balance-consistent, locale-correct, reproducible

DATAMIMIC supports two pseudonymization modes with different privacy postures:

Note on GDPR anonymization: Full anonymization status under GDPR depends on complete field coverage across all quasi-identifiers and a re-identification risk assessment on the complete record — not on individual field transformation alone. DATAMIMIC does not make anonymization claims on behalf of the customer. Non-seeded mode maximizes privacy at the transformation level; the customer is responsible for assessing re-identification risk across the full dataset.

In CE, PII fields are identified and modeled manually in the XML pipeline:

<setup>
  <generate name="customers" source="customer_export" target="customer_test">
    <key name="first_name"  converter="Mask" />
    <key name="email"       converter="anonymize_email" />
    <key name="iban"        converter="generate_iban" dataset="DE" rngSeed="42" />
    <key name="birth_date"  converter="shift_date" shiftDays="90" />
  </generate>
</setup>

datamimic run ./pseudonymize-customers/datamimic.xml

source is a controlled export or staging input — never a live production connection.

With rngSeed set: same source record → same pseudonymized output on every run. Stable for regression testing.

Without rngSeed: non-deterministic output — no reversible mapping exists at the field level. Stronger privacy posture for one-time delivery scenarios.

In the Enterprise Platform (EE): the DataWorkbench PII scanner automatically scans source schemas, assigns probability scores to each field, and flags candidates above a configurable threshold. Flagged fields are wired into the pseudonymization model automatically — no manual field mapping required.

<setup>
  <generate name="patients" count="1000" target="CSV">
    <variable name="patient" entity="Patient" dataset="US" ageMin="60" ageMax="80" rngSeed="42" />
    <key name="full_name"   script="patient.full_name" />
    <key name="age"         script="patient.age" />
    <array name="conditions" script="patient.conditions" />
  </generate>
</setup>

datamimic run ./patient-scenario/datamimic.xml

DATAMIMIC CE ships with a Model Context Protocol (MCP) server, making it directly callable from AI agents, Claude, Cursor, and any MCP-compatible runtime.

pip install datamimic-ce[mcp]

export DATAMIMIC_MCP_HOST=127.0.0.1
export DATAMIMIC_MCP_PORT=8765
export DATAMIMIC_MCP_API_KEY=your-key
datamimic-mcp

Agents can call generate with a domain, seed, count, and locale and receive deterministic, provenance-hashed output — making DATAMIMIC the natural test data runtime for agent-driven workflows.

import anyio, json
from fastmcp.client import Client
from datamimic_ce.mcp.models import GenerateArgs
from datamimic_ce.mcp.server import create_server

async def main():
    args = GenerateArgs(domain="person", locale="en_US", seed=42, count=2)
    payload = args.model_dump(mode="python")
    async with Client(create_server()) as c:
        a = await c.call_tool("generate", {"args": payload})
        b = await c.call_tool("generate", {"args": payload})
        # Determinism proof: identical hashes across calls
        assert (json.loads(a[0].text)["determinism_proof"]["content_hash"]
             == json.loads(b[0].text)["determinism_proof"]["content_hash"])

anyio.run(main)

📘 Full guide: docs/mcp_quickstart.md

Most teams adopt CE for one of three reasons. EE is not required for any of them.

1. Reproducible test data for CI/CD pipelines. Pin a seed against the generate_domain facade — or hand a seeded random.Random to any domain service — and you get byte-identical output across runs and machines. Both layers are gated on every CI run by tests_ce/architecture/. Regression tests stop being flaky because the input data is stable across runs.

from datamimic_ce.domains.facade import generate_domain

response = generate_domain({
    "domain": "person", "version": "v1", "count": 1,
    "seed": "ci-pipeline-42", "locale": "en_US",
    "clock": "2026-01-01T00:00:00Z",
})
# Same input → same output, every machine, every run.

2. Deterministic data backend for AI agents and LLM tooling. The bundled MCP server (pip install datamimic-ce[mcp]) exposes generate as an MCP tool. Agents call it with seed, locale, count; outputs ship with a determinism_proof.content_hash so the same call can be re-executed and verified later — useful for agent regression tests and for any workflow where the data the agent saw needs to be reconstructable.

3. Pseudonymization of staging and QA exports. Manual model in CE (XML pipeline), no scanner license required. Seeded mode for stable regression test data; non-seeded mode for one-time deliveries with maximized privacy posture. See the Pseudonymization section above.

DATAMIMIC produces evidence and reproducible artifacts that support compliance work. It does not replace your DPO, your CISO, or your auditor. The following are pointers for where DATAMIMIC outputs commonly slot into established programs:

Both editions produce reproducible artefacts. CE covers single-system fixtures and provenance evidence; multi-system audit evidence with role-based dashboards is EE.

These pointers do not constitute legal advice or a compliance attestation. Consult your DPO, CISO, or qualified counsel for formal compliance determinations. Full anonymization status under GDPR depends on re-identification risk across the complete dataset — see the pseudonymization disclaimer above.

CE and EE share the DATAMIMIC DSL and the determinism contract. The execution layer is separate: CE is a Python execution engine using multiprocessing (with optional Ray for distribution); EE is an independently-optimised execution engine with a Rust fastpath, ML/auto-regressive generation, keyset and manifest building from live schemas, and optimised distributed execution at billion-record scale.

╔══════════════════════════════════════════════════════════════════╗
║              DATAMIMIC ENTERPRISE PLATFORM (EE)                  ║
║                                                                  ║
║  ┌──────────────────────────────────────────────────────────┐    ║
║  │  PLATFORM LAYER                                          │    ║
║  │  UI · RBAC · Governance · Audit Dashboards               │    ║
║  │  DataWorkbench · PII Scanner · Pseudonymization Builder  │    ║
║  │  Scheduler · Task Runner · CI/CD · Template Engine       │    ║
║  └──────────────────────────────────────────────────────────┘    ║
║                                                                  ║
║  ┌──────────────────────────────────────────────────────────┐    ║
║  │  EE CORE  (separately maintained, more advanced than CE) │    ║
║  │                                                          │    ║
║  │  Rust fastpath for performance-critical paths            │    ║
║  │  ML / auto-regressive engine for complex distributions   │    ║
║  │  Keyset and manifest building from live DB schemas       │    ║
║  │  Optimised distributed execution at billion-record scale │    ║
║  │  Runtime profiles: Performance · Balanced · Flexibility  │    ║
║  │  Deep nested evaluation · Conditions · Rulesets          │    ║
║  │  Structured error catalog · Per-stage execution logging  │    ║
║  └──────────────────────────────────────────────────────────┘    ║
╚══════════════════════════════════════════════════════════════════╝

╔══════════════════════════════════════════════════════════════════╗
║              DATAMIMIC COMMUNITY EDITION (CE)  — this repo       ║
║                                                                  ║
║  Determinism Kit · Domain Services · Schema Validators           ║
║  Synthetic Generation · Pseudonymization (manual model)          ║
║  Python API · XML Pipelines · CLI · MCP Server                   ║
╚══════════════════════════════════════════════════════════════════╝

         ↓              ↓              ↓              ↓
    PostgreSQL       Oracle         MongoDB      CSV / JSON / XML

EE adds Kafka, EDIFACT, SWIFT MT, HL7 v2.x, and HL7 FHIR as additional targets — see Supported systems below. Both editions share the DATAMIMIC DSL and determinism contract.

All services are versioned and seeded; each generation emits a provenance hash suitable as evidence in audit reviews. Domain services can be used directly via constructor injection, or driven through the higher-level generate_domain({...}) facade for seed/locale/clock/count parameterisation (currently supports person, address, patient, doctor at v1).

# Initialize a new project
datamimic init ./my-scenario

# Validate an XML descriptor without executing it
datamimic validate ./my-scenario/datamimic.xml

# Run a scenario
datamimic run ./my-scenario/datamimic.xml

# Demos
datamimic demo list
datamimic demo create healthcare-example
datamimic demo create --all --target ./my_demos

# System and version info
datamimic info
datamimic version

See CONTRIBUTING.md. CE is MIT licensed and community contributions are welcome.

The CE engine is the foundation. If you are building integrations, domain extensions, or MCP tooling on top of DATAMIMIC, we want to hear from you.

MIT — see LICENSE.

The DATAMIMIC Enterprise Platform (EE) is a commercial product. Contact us for licensing.

DATAMIMIC — Deterministic, governed test data for regulated enterprises.

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