rapiddweller GmbH

How to create complex data for testing?

DATAMIMIC uses a model-based approach to synthetic data generation. Rather than scripting data by hand, our platform analyzes your source data (or a provided schema) to learn its statistical properties, distributions, and relationships. From this, it generates entirely new, deterministic synthetic data that mimics this complexity. For example, it can replicate intricate nested JSON structures while maintaining the relationships between customers and orders in a relational database. This referential integrity is critical for test validity and ensures data is realistic enough for even the most c

What is the difference between data anonymization and pseudonymization?

This is a critical distinction under regulations like GDPR. Anonymization alters data so individuals cannot be re-identified, even when combined with other information. This data is no longer considered personal data. Pseudonymization replaces direct identifiers (like a name) with a pseudonym (like a random user ID). The data can still be linked back to the individual with additional, separately kept information. Pseudonymous data is still considered personal data under GDPR. DATAMIMIC supports both techniques but excels at generating fully anonymized synthetic data, offering maximum privacy p

Is synthetic data as good as real data for testing?

For testing purposes, high-quality synthetic data often outperforms real data. A copy of production data provides a snapshot, but it carries real risks: it contains PII, often lacks edge cases, and reflects bias from the source. In contrast, model-generated synthetic data from DATAMIMIC preserves the statistical patterns of real data without the privacy risk. You can also augment synthetic datasets to add specific edge cases, balance classes to improve model training, and ensure comprehensive test coverage that production data alone might not provide.

How does DATAMIMIC help with GDPR and other data privacy regulations?

Using copies of production data for testing is a major compliance risk under GDPR, as it exposes sensitive personal data to a wider audience and increases breach risk. DATAMIMIC solves this by enabling a “privacy by design” approach. By generating synthetic test data that is statistically similar to production but contains no real PII, you remove the source of the risk entirely. This means your developers and testers get the realistic data they need to build and validate software, without ever accessing sensitive customer information. Your testing environments stay aligned with maj

Can DATAMIMIC work with our existing databases and CI/CD tools?

Yes. DATAMIMIC is built for enterprise ecosystems and designed for integration. It supports both SQL and NoSQL databases, including PostgreSQL, Oracle, and MongoDB, as well as streaming platforms like Apache Kafka. It also offers API endpoints to integrate directly into your CI/CD toolchain, including Jenkins, GitLab CI, and Azure DevOps. This enables fully automated data provisioning: fresh, compliant test data is delivered to your test environments as part of your normal build and deployment process, eliminating manual steps and delays.

Can DATAMIMIC run on-premise or in air-gapped environments?

Yes. DATAMIMIC runs completely offline, with no internet connection required at runtime. There is no telemetry, no license call-home, and no cloud dependencies. Deploy via podman-compose for single-host setups, or via Helm chart on OpenShift or Kubernetes for production clusters. Container images are small: server 250 MB, worker 750 MB, scheduler 150 MB. Updates follow your organization’s standard controlled-transfer process: pull new images, transfer them into your environment, and redeploy. This makes DATAMIMIC suitable for even the most restricted banking and public-sector environment

How does DATAMIMIC support DORA and BCBS 239 compliance?

DATAMIMIC produces deterministic, reproducible test data with full audit trails, directly aligned with the traceability, accuracy, and resilience testing requirements of DORA and the data lineage principles of BCBS 239. Every generation run is logged with task ID, timestamps, model version, and content hash. Tasks are replayable from the seed, so any dataset can be reconstructed months later with byte-identical output. When proof is missing, the system blocks the operation: no silent fallback. This gives your audit and risk teams the evidence they need without additional instrumentation.

How does DATAMIMIC work with AI agents like Claude and Cursor?

DATAMIMIC’s XML-based DSL is designed to be agent-friendly. We provide a Claude Code skill for the DATAMIMIC DSL, so AI agents can help developers write, validate, and lint data generation models directly in their editor. The important distinction: agents help developers work faster, but the generation itself stays fully deterministic, explainable, and auditable, never black-box ML output.