testdatatools
Developer tools · Model & scenario generation

DATAMIMIC CE

DATAMIMIC CE is a local Python runtime for model-based test data. XML models describe rules, relationships and output targets. It also offers Python and CLI interfaces for developer and CI workflows. 1

Sources reviewed

Manufacturer / project: rapiddweller

When does it fit?

Consider it for locally versioned XML models and explicit test cases. A values library may require less setup for a small fixture. 1

Fit still depends on your mandatory requirements.

What should you check before choosing?

PII field selection and transformation rules are manual. Keep versions and inputs fixed for replay; a seed alone does not establish identical results in every environment. The published SQL selection example does not prove automatic multi-table closure. 1 · 2

What must your PoC prove?

Generate a related parent/child fixture and one deliberate invalid case. Pin the runtime and dependencies. Repeat with explicit seed and reference clock, check business results separately from hashes, and try a clean local model restore.

Acceptance plan, not a product test result. Execution scope and limits.

Deployment and license

Install datamimic-ce with pip and run through Python or CLI in your own environment. Dependencies and cleanup remain your responsibility. 1

MIT-licensed CE. Enterprise licensing is separate. 1

Confirm the entitled version and operating requirements.

How you work with it

Version models and inputs, execute locally or in CI, then check the output. Enterprise adds shared project and task management. An exported Enterprise model needs CE-supported functions, inputs and dependencies; test it locally before assuming interchangeability. 1

Export does not establish compatibility with another runtime.

Documented capabilities

De-identification / maskingSpecify field replacements in the model
Subsetting / subset planningSQLite SQL row selection · CE 4.3.0
Data virtualizationUnclear

Documentation is not a measured benchmark. Unclear is missing evidence; the comparison matrix explains the gap.

Sources and scope

Sources can cover different releases; they do not establish measured performance.

Open the cited sources (6)
  1. rapiddweller/datamimic repository

    CE package installation, Python/CLI APIs, optional local MCP adapter, MIT license, manual pseudonymization and the explicit CE replay test boundary.

    Source checked: 2026-10-11
  2. Upgrade your models from DATAMIMIC 3.5 to 4.0

    Exact replay boundary; requires same engine version, complete deterministic inputs, explicit seed, execution topology, deterministic serialization and ordering; ML is excluded; date behavior depends on the seeded reference clock.

    Source checked: 2026-10-11
  3. Date and Time Generation

    Relative date windows use a runtime clock anchor; seeded execution uses deterministic runtime clock, while unseeded execution reads live clock.

    Source checked: 2026-10-01
  4. Database and other source selection contracts

    SQL selectors and source read semantics; distinguishes Platform-owned environment configuration from model execution.

    Source checked: 2026-10-01
  5. Iterate source traversal

    Source records and parent context for explicit child transformations and target generation.

    Source checked: 2026-10-01
  6. Vendor-authored CE 4.3.0 SQLite selection example

    Public executable CE 4.3.0 model uses SQL WHERE/ORDER BY to select source rows. Used only for the vendor’s own code example, not claims about Delphix. Not independently repeated here; no automatic graph-closure inference.

    Source checked: 2026-10-11

Use the same acceptance checks for your shortlist