Overcome All Your CI/CD Test Data Challenges with DataMaker
Test data bottlenecks slow your CI/CD pipelines and compromise quality, but DataMaker’s automated, secure solutions empower QA and DevOps teams to test smarter, faster, and with confidence.
Deliver Realistic Data for Every Test Scenario
Get production-like datasets tailored for functional, performance, security, and regression testing, ensuring robust, repeatable tests.
Support Testing Beyond Automation
Go beyond feeding test automation tools like Selenium or Cypress; manual exploratory testing is also supported.
Seamless Integration with CI/CD Workflows
Enjoy seamless integration with Jenkins, GitHub, GitLab, JUnit, and other testing frameworks for automated data provisioning and cleanup.
Isolated and Consistent
Isolate datasets for each test run to prevent conflicts in parallel testing and maintain consistency across builds, with versioned data.
How Datamaker Delivers Flawless CI/CD Test Data?
AI-Powered Test Data Template Creation:
Leveraging the DataMaker AI engine, you can describe your test data requirements in plain language using our intuitive AI Chatbot. Instead of generating data directly into CI/CD pipelines, DataMaker uses this input to quickly create reusable, production-grade test data templates. These templates are then integrated into your CI/CD workflows, ensuring fast, consistent, and version-controlled data provisioning at runtime.
Seamless Integration:
Integrate Datamaker effortlessly into your current CI/CD toolchain. We provide direct connectors for popular databases like PostgreSQL, MySQL, and SQL Server, and an API Bridge for any system with a RESTful API. Plus, Datamaker offers built-in compatibility with platforms such as SAP, ServiceNow, Jira, and Cypress, allowing you to access our AI Chatbot directly from your preferred testing environment.
Automated Masking & Anonymization:
Datamaker executes dynamic data masking, intelligent subsetting, and advanced anonymization techniques (like shuffling, substitution, and format-preserving encryption) directly on your production data. This ensures sensitive information is rigorously protected and compliant with regulations such as GDPR and HIPAA, all while preserving data realism for effective testing.
Isolated & Consistent Data Environments:
Datamaker creates dedicated, pristine data environments or snapshots for each test run. This fundamental capability guarantees that every test operates on an isolated, consistent dataset. It eliminates data conflicts from parallel execution and ensures highly reliable, repeatable test results, dramatically simplifying defect reproduction.
Scalable & Constraint-Aware Data:
DataMaker AI is versatile, capable of generating diverse volumes and types of realistic data for comprehensive functional, performance, and integration testing. Crucially, it strictly adheres to the precise data types, formats, validation rules, and constraints defined within your enterprise systems (e.g., SAP, Salesforce), ensuring generated data is always valid and fit for purpose at any scale.
Centralized & End-to-End Control:
Datamaker's Team Projects provide a centralized, organized workspace for all your test data. This enables seamless team collaboration with shared access and Role-Based Access Control (RBAC). It also offers robust versioning, auditing, and automated cleanup, optimizing your entire data lifecycle and scaling effortlessly with your team.
Why Choose DataMaker for CI/CD Test Data Over Traditional Approaches?
Integration with CI/CD Pipelines
Requires custom scripting and manual triggers; fragile and time-consuming.
Partial support via plugins or middleware; setup is often complex.
Natively integrates with CI/CD tools like Jenkins, GitLab, and GitHub for automated provisioning and cleanup.
Speed of Data Provisioning
Provisioning is manual and slow; delays are common during test runs.
Faster than manual, but still requires coordination and lead time.
Instantly provisions data at pipeline runtime using AI, with zero wait time for test data.
Test Isolation for Parallel Execution
Test environments often share data, leading to collisions and flaky tests.
Some support for isolation, but the setup is manual and error-prone
Automatically creates isolated, versioned datasets for each test run to ensure consistency and prevent conflicts.
Data Consistency Across Builds
Data varies between runs; hard to reproduce bugs or maintain reliability.
Supports consistent data, but requires careful version control.
Built-in versioning and repeatable data environments ensure consistent test results across builds.
Scalability for Frequent Test Runs
Doesn’t scale for high-frequency or large-scale pipelines.
Can scale, but performance and cost may be limiting.
Easily supports high-volume, high-frequency test runs across teams and environments.
Automation of Data Cleanup
Cleanup must be done manually; risk of leftover test data impacting future runs.
Some automated cleanup, but often tied to specific platforms.
Automates full data lifecycle; provisioning, use, and cleanup, within the CI/CD workflow.
Scalability for Frequent Test Runs
Doesn’t scale for high-frequency or large-scale pipelines.
Can scale, but performance and cost may be limiting.
Easily supports high-volume, high-frequency test runs across teams and environments.
Automation of Data Cleanup
Cleanup must be done manually; risk of leftover test data impacting future runs.
Some automated cleanup, but often tied to specific platforms.
Automates full data lifecycle; provisioning, use, and cleanup, within the CI/CD workflow.
Ready to stop letting test data slow down your pipeline?
