Intelligent, Model-Based Test Data Generation
Speed up QA and safeguard compliance with AI-assisted, model-based synthetic test data generation. Connect to your databases or upload JSON, define relationships once, and generate realistic, privacy-safe datasets that preserve referential integrity across systems — ready for CI/CD. Built for GDPR-aligned, trusted data solutions and modern test data management; start from docs or use the API to automate across environments.
Key Benefits
Define structures/constraints; enforce referential integrity across datasets for consistent test data management in complex apps
Create realistic, purpose-fit synthetic data quickly for QA, staging & integration testing without exposing production records.
Advanced handling of complex JSON and XML including deeply nested, API-style payloads used in microservices.
Privacy-aware workflows; built for regulated teams that must stay GDPR/financial-services compliant with anonymization and pseudonymization.
Automate generation in pipelines via REST and run headless in DevOps/CI to seed test environments on every build.
A clear layer to inspect quality checks & relations so data stewards can validate models in a data-quality framework.
How it works
DATAMIMIC connects directly to your databases or ingests files like JSON to auto-generate a model of your data, then lets you refine entities and relationships so generated datasets stay consistent across tables, NoSQL collections and deeply nested JSON/XML. Because it’s model-based and AI-driven, outputs preserve referential integrity and support trusted data solutions for test, dev and training. Through documented REST/OpenAPI endpoints you can run it headless in CI/CD to keep test environments consistently seeded without copying production data.
Use cases
Teams use DATAMIMIC to build realistic, privacy-compliant datasets for QA and staging without exposing production records, to run integration and end-to-end tests that depend on stable cross-entity links (customers ↔ orders, payments ↔ accounts), and to produce API-ready, JSON/XML payloads for microservice and banking/fintech scenarios — all inside a GDPR-compliant, synthetic-data workflow.
Ship faster with privacy-safe test data that mirrors your systems.
DATAMIMIC generates realistic, synthetic datasets from a model of your structures, so relationships stay intact across tables and even deeply nested JSON/XML. Because it’s built for regulated teams and GDPR-friendly workflows, you can test without exposing production data—and you can trigger generation headlessly via REST/OpenAPI in CI/CD to keep staging consistently seeded.
Deploy DATAMIMIC as SaaS or on-premise—via Docker/Podman or Helm on Kubernetes/OpenShift—and use the visual modeling UI to inspect entities, define data-quality checks, and enforce referential integrity; then automate generation through the REST API with project access tokens, or keep projects versioned by syncing a DATAMIMIC project to a specific Git branch so test-data definitions stay in lockstep with your codebase
See DATAMIMIC in action
Explore real projects where teams used model-based synthetic data to move faster and stay compliant — from tier-1 banks that cut test-data cycles from weeks to hours across Oracle, MongoDB and Kafka, to education and public-sector platforms anonymizing millions of records per hour. See how trusted data solutions from DATAMIMIC removed the need for manual masking while keeping data realistic enough for E2E and API tests.
Automate in your pipeline
Hook DATAMIMIC into your delivery process with the documented REST/OpenAPI endpoints, run it headless to provision synthetic data on every build, and even sync projects to a Git branch so test-data definitions stay in lockstep with your codebase. This lets DevOps teams enforce repeatable, GDPR-compliant test data and ship under regulatory pressure with trusted data solutions. For trials or support, contact the team.
Frequently Asked Questions.
How to create complex data for testing?
DATAMIMIC employs a model-based approach to synthetic data generation. Rather than just scripting data, our AI first analyzes your source data (or a provided schema) to learn its statistical properties, distributions, and relationships. Subsequently, it generates entirely new, synthetic data that mimics this complexity. For instance, it can replicate intricate nested structures in JSON while also maintaining the relationship between customers and orders tables in a relational database. Importantly, this capability—maintaining referential integrity—is critical for the validity of test data and ultimately ensures the data is realistic enough for even the most complex test scenarios.
What is the difference between data anonymization and pseudonymization?
This is a critical distinction under regulations like GDPR. Specifically, data Anonymization alters data so individuals cannot be re-identified, even when combined with other information. Thus, this data is no longer considered personal data. Pseudonymization, on the other hand, replaces direct identifiers (like a name) with a pseudonym (like a random user ID). However, the data can still be linked back to the individual with the use of additional, separately kept information. Therefore, pseudonymous data is still considered personal data under GDPR. DATAMIMIC supports both techniques but excels at generating fully anonymized synthetic data, offering maximum privacy protection by design.
Is synthetic data as good as real data for testing?
For testing purposes, high-quality synthetic data often outperforms real data. Specifically, while a copy of production data provides a perfect snapshot, it nonetheless carries inherent risks as it contains PII, often lacks completeness and specific edge cases, and moreover exhibits bias. In contrast, AI-generated synthetic data, like that from DATAMIMIC, maintains the statistical accuracy and patterns of real data without the privacy risks. This directly addresses the ‘synthetic data vs real data’ consideration. Furthermore, you can subsequently augment synthetic datasets to create specific edge cases, additionally balance classes to improve model training, and ultimately ensure comprehensive data quality and test coverage that production data might not provide on its own.
How does DATAMIMIC help with GDPR and other data privacy regulations?
Using copies of production data for testing and development is a major compliance risk under GDPR, as it unnecessarily exposes sensitive personal data to a wider audience and increases the risk of a data breach. DATAMIMIC solves this fundamental problem by enabling a “privacy by design” approach. Through generating synthetic test data that is statistically identical to production but contains no real PII, you remove the source of the risk entirely. This means your developers and testers get the high-quality, realistic data they need to build and validate software effectively, without ever accessing sensitive customer information. In this way, this ensures your testing environments are inherently compliant with major data protection regulations.
Can DATAMIMIC work with our existing databases and CI/CD tools?
Absolutely. DATAMIMIC is built for modern enterprise ecosystems and particularly designed for seamless integration. Notably, it provides broad support for both SQL and NoSQL databases, allowing you to connect to your existing data sources with ease. In addition, it offers API endpoints to integrate directly into your data pipeline and CI/CD toolchain (e.g., Jenkins, GitLab CI, Azure DevOps). Through this approach, it enables fully automated data provisioning, a core tenet of modern Test Data Management, where fresh, compliant test data is delivered to your test environments as part of your automated build and deployment processes, thus eliminating manual effort and delays.