Datamimic – don't let your coding agent invent its own test world

DATAMIMIC Community Edition, an MIT-licensed Python-native synthetic data engine from rapiddweller, is now MCP-ready and ships an AGENTS.md contract that directs coding agents to preserve intent as model.dm.json and stop only on verified=true. The CE release generates deterministic synthetic datasets and performs PII-aware pseudonymization across PostgreSQL, MySQL, Oracle, MS SQL, SQLite, MongoDB, CSV, JSON, XML, XLSX, DbUnit, and fixed-width formats, while the DATAMIMIC Enterprise Platform adds a probability-scored PII scanner, multi-system execution across Oracle, MongoDB, and Kafka, and EDIFACT, SWIFT MT, HL7 v2.x, and HL7 FHIR message templates. The company says the platform is deployed in regulated EU banking environments, with reference customers available under NDA.

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. 👉 Enterprise Platform: datamimic.io https://datamimic.io | 📘 Docs: docs.datamimic.io https://docs.datamimic.io | 📅 Book a strategy call: datamimic.io/contact https://datamimic.io/contact 🤖 AI agent? Start at AGENTS.md https://github.com/rapiddweller/datamimic/blob/development/AGENTS.md and use the project CLI: preserve new intent as model.dm.json , submit an early best attempt via datamimic scaffold ... --format json , repair from the structured issues, declare an expectation per stated requirement, and stop on verified=true . Existing raw XML uses lint plus bounded dry-run. 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 · XLSX · DbUnit · fixed-width .fcw - Model behavior — weighted state machines, composite multi-field references, control flow <while , <assert , and a scriptable memstore for staged aggregation - Emit provenance — append-only execution logs and per-output content hash for audit re-execution - Guide agents — machine-readable capabilities, progressive reference queries, and one canonical CLI scaffold transaction; an optional MCP adapter exposes the same authoring service 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 https://datamimic.io . The CLI is the baseline agent contract. Install CE with pip install datamimic-ce ; inside this checkout, use .venv/bin/datamimic so a stale global installation cannot change the available schema or commands. | Need | CLI tool | Contract | |---|---|---| | Discover the live structural surface | datamimic capabilities | Compact machine-readable JSON index by default; --full for the complete manifest, --section <name for one section. | | Learn the Intent Model progressively | datamimic reference authoring , then datamimic reference authoring --category <category --kind <kind | Start with the query catalogue, then load only the typed fragment needed. | | Author a new model | Preserve model.dm.json ; run datamimic scaffold model.dm.json --format json | One compile/lint/bounded-run/acceptance transaction per changed attempt. Stop on verified=true ; generated XML is runtime output. | | Work with existing raw XML | datamimic lint model.xml --format json , then datamimic dry-run model.xml --format json | Fix diagnostics, inspect bounded samples for intent, then use datamimic run model.xml only when real execution is requested. | | Find a DSL detail | datamimic reference overview , then a narrow reference topic/name | Query the live model and rule registries instead of guessing elements, generators, scope, distributions, or rules. | capabilities , authoring-reference projections, and the commands shown with --format json return machine-readable JSON. On a failed scaffold attempt, change model.dm.json using its structured validation issues, typed repair, or rule diagnostics before retrying. A typed max count remediation instead changes only the bounded scaffold parameter to at least its reported minimum. Never repeat an identical failed call. A successful scaffold result is terminal for authoring, so do not lint or dry-run its generated XML again. Exact source fragments are discoverable through queries such as --category source --kind memstore . When the calling environment already exposes DATAMIMIC MCP tools, they map to the same canonical contracts and implementations: reference → datamimic reference , scaffold → datamimic scaffold , lint → datamimic check , and dry-run → datamimic run . Install the adapter with pip install "datamimic-ce mcp " ; registration details belong in the MCP quickstart https://github.com/rapiddweller/datamimic/blob/development/docs/mcp quickstart.md , not in the authoring workflow. The adapter intentionally exposes only the four canonical reference, scaffold, check, and bounded-run operations; domain generation remains a Python/CLI capability rather than a parallel MCP authoring path. Author and verify a new model Create the dataset I describe with DATAMIMIC. Read AGENTS.md first. In a repository checkout use .venv/bin/datamimic ; otherwise use the current datamimic CLI. Preserve my intent as model.dm.json ; do not hand-write XML. Start from the minimal valid document shape in AGENTS.md "Authoring a new model" . Two rules prevent most rejections: the top level allows ONLY version, seed, products, expectations; product-level "kind" generated/source/time series is a different vocabulary from field-level "kind" increment, values, weighted, int range, decimal range, pattern, constant, script . Range fields take minimum/maximum, never min/max. Submit EARLY: run datamimic scaffold model.dm.json --format json with your best attempt after at most one discovery call. Repair from the structured issues path/code/message/allowed fields and diagnostics fix hint — they teach the schema faster than more discovery. Never resubmit an unchanged document. If a remediation requests a larger max count, retry scaffold with at least that value without changing the intent. Declare an expectation for every requirement I state counts as exact count with a "count" field, uniqueness, allowed values, ranges, foreign keys — verified=true certifies only what you declared. Stop on verified=true; do not lint or dry-run the generated XML. If I request real execution, save the returned XML as a generated artifact and run that descriptor. Return the model.dm.json path and concise verification evidence. Relational hierarchy with referential integrity fully supported — no XML needed Seed a relational dataset with referential integrity: 4 customers, each with exactly 2 orders. Customers get an incrementing unique id and a region from {north, south, east, west}. Each order carries the REAL parent customer id as a foreign key and an amount between 10.0 and 500.0. Follow AGENTS.md's "Authoring a new model" and its structural recipes: orders nest inside the customer product's "children" array; the FK field is {"kind": "script", "script": "parent.id"} with a foreign key role — a randomly generated FK passes schema validation but fails per-parent-count acceptance. Declare expectations for the customer count, customer id uniqueness, exactly 2 orders per customer per parent count , the orders- customers foreign key, and the amount range. Stop on verified=true and show the acceptance evidence. Raw XML remains supported for existing descriptors lint → dry-run → run; see AGENTS.md . For new models it is a last resort: only when a scaffold issue explicitly classifies the requirement as unsupported intent should an agent hand-author XML, preserving that evidence. 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. | Capability | Community Edition CE | Enterprise Platform EE | |---|---|---| | Deterministic data generation | ✅ | ✅ | | Deterministic seeding in the DSL | ✅ entities + standalone literal <key generator 4.0.0 | ✅ same, plus sandboxed script expressions and stdlib random calls | | Pseudonymization — seeded GDPR Art. 4 5 ; supports Art. 25 / Art. 32 | ✅ manual model | ✅ automated via DataWorkbench | | Pseudonymization — non-seeded privacy-maximized | ✅ manual model | ✅ automated via DataWorkbench | | Python API + XML pipelines | ✅ | ✅ | | Domain models: Finance, Healthcare, Demographics | ✅ | ✅ | | Time-series generation <generate start/end/interval , ISO 8601, prefix-stable | ✅ | ✅ | | MCP server for AI agent integration | ✅ | ✅ | | CLI + local execution | ✅ | ✅ | | Scale | millions of records via Python multiprocessing and optional Ray | designed for billion-record workloads — Rust fastpath, optimised multi-process execution, and keyset/manifest building on top of the shared Ray distribution layer | | PII scanner | ❌ | ✅ probability-scored field detection, configurable threshold, DataWorkbench integration | | Runtime configuration profiles | ❌ | ✅ Performance · Balanced · Flexibility | | Memory management | standard | optimised for high-volume batch and streaming | | Logging granularity | flat execution log | configurable: minimal · standard · deep nested tracing | | Nested structure evaluation | basic | deep nested generation with extended condition + ruleset evaluation | | Importer / exporter logging | ❌ | per-stage logging for importers and exporters | | Error handling | standard exceptions | structured error catalog with recovery strategies | | Rust fastpath | ❌ | performance-critical paths in Rust | | Keyset and manifest building | ❌ | reads live DB schemas to build coordinated multi-table generation plans | | ML / auto-regressive engine | ❌ | combine statistical models with conditions, rulesets, validators for complex distributions | | Capability | EE | |---|---| | Multi-user collaboration | ✅ | | Role-based access control RBAC | ✅ | | Audit logs + provenance dashboards | ✅ | | PII scanner — probability scoring, threshold-based field flagging | ✅ | | DataWorkbench — visual field mapping and pseudonymization model builder | ✅ | | Reusable enterprise template library | ✅ | | Scheduled execution + task runner | ✅ | | CI/CD pipeline integration Tosca, Jenkins, GitLab | ✅ | | Multi-system execution: Oracle, MongoDB, Kafka | ✅ | | Template engine: schema-aware editors for EDIFACT, SWIFT MT, HL7 v2.x, and HL7 FHIR — customer-uploadable specs, further industry formats built per engagement on the same framework | ✅ | | Audit-evidence artefacts for GDPR Art. 30 records, PCI DSS 4.0 Req. 6.5.5 test data reviews, and — for US Covered Entities / Business Associates — HIPAA §164.312 evidence packs | ✅ | | On-premise deployment + air-gapped environments | ✅ | | LSP-powered IDE tooling for DSL authoring | ✅ | 👉 Explore the Enterprise Platform https://datamimic.io | Book a platform demo https://datamimic.io/contact The EE core supports three runtime configuration profiles, selectable per execution context: | Profile | Optimises for | Typical use case | |---|---|---| | Performance | Maximum throughput via Rust fastpath, optimised multi-process execution, and Ray-based distribution | Bulk generation at billion-record volumes to PostgreSQL, Oracle, Kafka | | Balanced | Throughput + full audit logging | Standard enterprise pipeline runs with compliance requirements | | Flexibility | Deep nested evaluation, extended condition and ruleset processing | Complex domain models with ML engine combinations, multi-level referential structures | 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 | Format | Coverage | |---|---| | UN/EDIFACT | Schema-aware form editor; spec versions and subsets per engagement | | SWIFT MT | Schema-aware form editor; categories and SR versions per engagement | | HL7 v2.x | Schema-aware form editor; versions per engagement | | HL7 FHIR | Schema-aware form editor for FHIR resources Patient, Observation, Encounter, … ; profiles per engagement | | Further industry formats ISO 20022 / MX, vertical dialects | Built into the editor catalogue per customer engagement, on the same framework | 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 supported-systems below. | Role | What DATAMIMIC solves | |---|---| | QA / Test Manager | Eliminate manual test data requests. Self-service, governed, always ready. | | Business Analyst | Define data requirements in business-readable models — no scripting needed. | | Platform / DevOps Engineer | Integrate deterministic test data generation into CI/CD and scheduled pipelines. | | Compliance / Audit | Full audit trail for every generation run. Regulator-ready logs, no production data exposure. | | Enterprise Architect | One governed standard across Oracle, MongoDB, Kafka, flat files, and custom systems. | 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 engine version + same model + same seed = 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 https://github.com/rapiddweller/datamimic/blob/development/tests ce/architecture/test service replay determinism.py . - DSL-level seeding: <setup rngSeed="N" makes the whole model deterministic — every seed-less <variable entity="…" derives a reproducible child RNG from it, and <variable rngSeed="…" overrides it for that block no seed anywhere → wall-clock random . Verified by tests ce/integration tests/test determinism seed scenarios https://github.com/rapiddweller/datamimic/tree/development/tests ce/integration tests/test determinism seed scenarios . As of 4.0.0 the same seed also reaches standalone literal generators <key generator="…" , typed/pattern keys, DateTimeGenerator , and cross-page unique picks — machine-independently. - Source reads: distribution="ordered" reads a data source in stable file order; distribution="random" shuffles but replays identically when <setup rngSeed is set without a seed the shuffle is non-deterministic by design, for privacy-maximized one-time deliveries . Deterministic shuffling across distributed / multi-process execution is EE. - 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/ : spawn rng reproducible child-RNG derivation , now utc naive , and resolve clock . The same contract vocabulary the Enterprise Platform enforces end-to-end. The Enterprise Platform EE goes further: beyond the CE contract, EE makes the whole execution environment deterministic — a configurable/frozen wall-clock not just CE's fixed anchor , and deterministic SAFE GLOBALS plus the Python random functions, so sandboxed script expressions and any stdlib random call replay identically as well. python 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: python 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 | Scope | CE | Enterprise Platform | |---|---|---| | Facade generate domain registered domains | ✅ byte-identical, CI-gated | ✅ byte-identical | | Domain services direct use with seeded rng=... | ✅ byte-identical, CI-gated | ✅ byte-identical | | Literal generators with seeded rng=... | ✅ byte-identical | ✅ byte-identical | | RNG / clock runtime SPOTs | ✅ spawn rng , now utc naive , resolve clock | ✅ same contract, enforced end-to-end | | Architecture gates in CI | ✅ facade replay + service replay every service + clock drift | ✅ 5+ gates RNG ownership, clock drift, DSL eval, seeded-mode propagation, dataset SPOT | | Custom XML pipelines seeded via <setup rngSeed | ✅ byte-identical, machine-independent single-process | ✅ byte-identical, distributed | | Multi-system coordinated execution Oracle + MongoDB + Kafka in one run | — | ✅ byte-identical end-to-end | | Seeded vs unseeded pseudonymization deterministic clock anchor vs CSPRNG live-clock | — | ✅ | | Threat-led / TLPT-grade audit evidence full contract enforcement, per-stage execution logging | — | ✅ | CE delivers contract-enforced determinism for the synthetic-data generation surface facade, services, generators and, as of 4.0.0, for seeded XML descriptors — byte-identical across machines, executed single-process. The Enterprise Platform extends the same contract to distributed and multi-system execution with referential integrity and the seeded/unseeded pseudonymization modes, and adds the five drift-gates that lock the contract end-to-end for regulated deployments. | | Faker / Random generators | DATAMIMIC CE | DATAMIMIC EE | |---|---|---|---| | Reproducible output | ❌ | ✅ | ✅ | | Domain-aware relationships | ❌ | ✅ | ✅ | | Business logic constraints | ❌ | ✅ | ✅ | | Per-output provenance hash | ❌ | ✅ | ✅ | | Source data pseudonymization | ❌ | ✅ manual | ✅ automated | | PII field detection | ❌ | ❌ | ✅ probability-scored | | Enterprise governance layer | ❌ | ❌ | ✅ | | Multi-system execution | ❌ | ❌ | ✅ | | Role-based workflows | ❌ | ❌ | ✅ | | Designed for regulated-industry deployment governance, audit, RBAC | ❌ | ❌ | ✅ | python 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 with a seed import random from datamimic ce.domains.healthcare.services import PatientService patient = PatientService rng=random.Random 42 .generate print f"{patient.full name}, {patient.age}, {patient.conditions}" Age-appropriate, domain-consistent — and identical every run with a fixed seed pip install datamimic-ce python import random from datamimic ce.domains.healthcare.services import PatientService patient = PatientService rng=random.Random 42 .generate print patient.full name, patient.age, patient.conditions Age-appropriate conditions, demographically realistic; deterministic with a seed python import random from datamimic ce.domains.finance.services import BankAccountService account = BankAccountService rng=random.Random 42 .generate print account.account number, account.balance Balance-consistent, locale-correct; reproducible with a seed DATAMIMIC supports two pseudonymization modes with different privacy postures: | Mode | How | Legal classification | Use case | |---|---|---|---| | Seeded rngSeed set | Deterministic, reproducible | Pseudonymization GDPR Art. 4 5 | Regression testing, stable CI/CD pipelines | | Non-seeded no rngSeed | Non-deterministic, no reversible mapping at field level | Privacy-maximized transformation | One-time data delivery, higher privacy posture | 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 defaultSeparator="," <generate name="customers" source="customer export.csv" target="CSV" distribution="ordered" < -- distribution="ordered" reads the source in a stable order — required so the Nth source row maps to the same seeded synthetic value on every run. The default "random" shuffles non-deterministically and would break it. rngSeed on the <variable makes the synthetic values reproducible; drop rngSeed for the privacy-maximized non-deterministic mode. -- <variable name="p" entity="Person" dataset="DE" rngSeed="42" / <variable name="acc" entity="BankAccount" dataset="DE" rngSeed="42" / <key name="first name" script="p.given name" / <key name="last name" script="p.family name" / <key name="email" script="p.email" / <key name="iban" script="acc.iban" / <key name="birth date" script="p.birthdate" / </generate </setup Built-in converters can additionally transform a key's value — e.g. irreversibly hash the original instead of replacing it, or partially mask it: <key name="email" script="p.email" converter="Hash 'sha256','hex' " / <key name="iban" script="acc.iban" converter="MiddleMask 8, 4 " / Available converters 13 : Mask , MiddleMask start, end , CutLength n , Substring start, end , JavaHash , RemoveNoneOrEmptyElement , Hash type, format , salt , DateFormat fmt , Append , UpperCase , LowerCase , Date2Timestamp , Timestamp2Date . 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 Any <generate becomes a time-series loop when given strict ISO 8601 start / end / interval attributes. Per iteration the script context exposes a ts namespace: | Variable | Type | Meaning | |---|---|---| | ts.now | datetime | Current tick | | ts.step | int | Position within one series 0..N-1 | | ts.series | int | Which series this row belongs to 0..count-1 | Output column names — including whether to even emit a timestamp or series-id column — are entirely the user's choice via <key . The primitive is domain-agnostic; the same DSL covers IoT readings, financial ticks, log streams, smart meters, anything time-indexed. <setup < -- Stock ticks: three symbols, 5-min interval, 30-min window writes ticks.csv -- <generate name="ticks" count="3" start="2026-01-01T09:30:00+00:00" end="2026-01-01T10:00:00+00:00" interval="PT5M" target="CSV" <key name="timestamp" script="ts.now.isoformat "/ <key name="symbol" script=" 'AAPL','MSFT','GOOG' ts.series "/ <key name="price" script="100 + ts.step 0.25"/ </generate < -- Sensor with diurnal seasonality, single series count defaults to 1; writes readings.csv -- <generate name="readings" start="2026-01-01T00:00:00+00:00" end="2026-01-08T00:00:00+00:00" interval="PT1H" target="CSV" <key name="timestamp" script="ts.now.isoformat "/ <key name="value" script="20 - 10 math.cos ts.now.hour math.pi / 12 "/ </generate </setup Guarantees: - Prefix-stable by construction — the first N ticks of series 0 are byte-identical regardless of total window length, because each row's ts.now is a pure function of start + interval step . - Loop order is contiguous per series — series 0's full sequence, then series 1's, etc. Makes downstream grouping trivial. - Strict ISO 8601 — start / end via datetime.fromisoformat Z-suffix supported ; interval via the isodate https://pypi.org/project/isodate/ library PT1H , PT15M , PT5S , P1D , P1W , P1DT12H , fractional seconds for sub-second precision . Resolution: PT0.001S = 1 ms, PT0.000001S = 1 µs Python datetime.timedelta microsecond floor; sub-µs intervals and constant-length-undefined units like months/years are rejected with a clear error . - count is orthogonal , not overloaded — it means "outer-loop iterations of this <generate " in all modes same as nested <generate count=… . In time-series mode each outer iteration is one series of N ticks, so total rows = count × ticks per series . Default count="1" keeps single-series fixtures terse. - Naming caveat — a <key name="ts" output column would shadow the namespace current product overrides current variables in script scope , and a <variable name="ts" is rejected at parse time. Use a different name, e.g. timestamp for the column. Composes with the existing <variable mechanism for multi-source merges e.g. join each tick with a sensor-metadata CSV via <variable source="meta.csv" cyclic="True" inside the same <generate , with <key condition="..." filtering, and with <nestedKey sub-scopes — the ts namespace is visible everywhere a <key script runs. See tests ce/integration tests/test timeseries/ for committed DSL fixtures + proofs including pagination invariance . 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/ https://github.com/rapiddweller/datamimic/tree/development/tests ce/architecture/ . Regression tests stop being flaky because the input data is stable across runs. python 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 engine version + same model + same seed → same output, every machine, every run. 2. Deterministic data backend for AI agents and LLM tooling. The CLI and Python API are the baseline surfaces for seeded, verifiable generation. The optional MCP adapter pip install "datamimic-ce mcp " exposes the canonical reference, scaffold, check, and bounded-run authoring operations. Generated domain-facade outputs include a determinism proof.content hash , so Python/CLI callers can re-execute and verify the data later — useful for agent regression tests and any workflow where the data an agent saw must 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 pseudonymization--ce-manual-model . 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. | Regulation / standard | Where DATAMIMIC contributes | |---|---| | DORA Reg. 2022/2554 — Art. 24 testing of ICT tools, systems and processes; non-TLPT scope | Reproducible test datasets for non-TLPT resilience tests; deterministic data fixtures for ICT testing programmes | | ISO/IEC 27701:2019 — A.7.2.8 records related to processing PII and A.7.4.5 PII minimisation | Synthetic data in lieu of PII in non-production environments; documented model definitions as supporting evidence | | HIPAA Security Rule — §164.312 technical safeguards US Covered Entities / Business Associates only | Synthetic Patient/MedicalDevice/MedicalProcedure data for dev and test environments without ePHI exposure | | GDPR — Art. 4 5 pseudonymization definition; Art. 25 privacy by design; Art. 32 security of processing | Seeded pseudonymization with deterministic mapping; non-seeded mode for stronger privacy posture | | PCI DSS 4.0 — Req. 6.5.5 live PANs prohibited in test/development | Synthetic PAN generation for test environments; deterministic tokenisation reproducible across runs | 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 pseudonymization--ce-manual-model . 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 supported-systems below. Both editions share the DATAMIMIC DSL and determinism contract. | System | CE | EE | Notes | |---|---|---|---| | PostgreSQL | ✅ | ✅ | EE adds schema introspection and referential integrity | | MySQL | ✅ | ✅ | | | Oracle | ✅ | ✅ | EE production-validated in regulated banking environments | | MS SQL Server | ✅ | ✅ | | | SQLite | ✅ | ✅ | Lightweight CI/CD fixtures | | MongoDB | ✅ | ✅ | EE adds nested document generation | | CSV / JSON / XML | ✅ | ✅ | Flat file pipelines | | XLSX | ✅ | ✅ | Spreadsheet read + write first row = header | | DbUnit XML | ✅ | ✅ | .dbunit.xml dataset read + write | | Fixed-width .fcw | ✅ | ✅ | Self-describing column files, read + write | | Apache Kafka | — | ✅ | Real-time streaming, payment scenarios | | HL7 v2.x | — | ✅ | Test/training output via template engine | | HL7 FHIR | — | ✅ | Test/training output via template engine | | EDIFACT / SWIFT MT | — | ✅ | Test/training output only; does not satisfy SWIFT CSCF v2025 secure-zone controls 1.1 environment protection, 1.4 internet restriction . Generated messages must not be transmitted from a CSP-attested secure zone. | | Domain | Services available | |---|---| | Healthcare | Patient, Doctor, Hospital, MedicalDevice, MedicalProcedure | | Finance | Bank, BankAccount, CreditCard, Transaction | | Insurance | InsuranceCompany, InsuranceProduct, InsurancePolicy, InsuranceCoverage | | E-commerce | Order, Product | | Public sector | AdministrationOffice, EducationalInstitution, PoliceOfficer | | Demographics | Person DE / US / VN locale packs , Address, City, Country | | Common | Company | 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 . Discover the live structural surface as JSON datamimic capabilities Enumerate typed authoring queries, then request only the needed fragment datamimic reference authoring datamimic reference authoring --category field --kind weighted datamimic reference overview Compile and fully verify the canonical intent artifact; stop on verified=true datamimic scaffold model.dm.json --format json Lint a descriptor: schema, semantics, best practices — every finding carries a rule id DMxxx and a fix hint. Exit codes 0/1/2. datamimic lint my-scenario/datamimic.xml datamimic lint my-scenario/datamimic.xml --format json diagnostics v1, CI-friendly Safely execute bounded counts with neutralized targets and sample rows datamimic dry-run my-scenario/datamimic.xml --format json Run a verified scenario for real datamimic run my-scenario/datamimic.xml Initialize a new project datamimic init my-scenario Demos datamimic demo list datamimic demo create demo-healthcare datamimic demo create --all --target ./my demos System and version info datamimic info datamimic version | Resource | Link | |---|---| | Full documentation | docs.datamimic.io https://docs.datamimic.io | | MCP quickstart | docs/mcp quickstart.md https://github.com/rapiddweller/datamimic/blob/development/docs/mcp quickstart.md | | Developer guide | docs/developer guide.md https://github.com/rapiddweller/datamimic/blob/development/docs/developer guide.md | | Enterprise platform | datamimic.io https://datamimic.io | | GitHub Discussions | Discussions https://github.com/rapiddweller/datamimic/discussions | | Issue tracker | Issues https://github.com/rapiddweller/datamimic/issues | | Email support | support@rapiddweller.com mailto:support@rapiddweller.com | See CONTRIBUTING.md https://github.com/rapiddweller/datamimic/blob/development/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 https://github.com/rapiddweller/datamimic/blob/development/LICENSE . The DATAMIMIC Enterprise Platform EE is a commercial product. Contact us https://datamimic.io/contact for licensing. DATAMIMIC — Deterministic, governed test data for regulated enterprises.