Part ofProtection and Enforcement
Test Data Management
Prepare Useful Data for Development and Testing
Build a test dataset from de-identified production data, newly generated records, or both, while keeping the structure applications need.
- Choose the Data Approach
- Keep Related Records Usable
- Refresh Environments on Schedule
The test-data problem
Realistic testing should not require unrestricted production data
Development and QA teams need data that behaves like production data, but unrestricted copies create unnecessary exposure outside production.
DataSunrise prepares a controlled target by replacing sensitive values in selected production data, generating new records, or combining both approaches. Teams can validate the result and repeat the task whenever an environment needs fresh data.
How it works
Prepare a test dataset in three steps
Define what the environment needs, choose how to create the data, then validate and deliver the result.
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Scope
Define the target
Choose the target tables, required row counts, and the relationships the test environment must preserve.
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Data
Mask, generate, or combine
Replace sensitive production values, create new records, or use separate tasks for both approaches.
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Delivery
Validate and refresh
Preview available output, load the target, review status and errors, and rerun the task when fresh data is needed.
Keep the Dataset Useful and Repeatable
DataSunrise reads the selected table structure and helps prepare values that fit each field. Teams can use built-in or custom generators, preview generated values, enforce uniqueness, and account for records already in the target.
Table Relations can keep related masked fields aligned. For newly generated rows, foreign-key generation helps preserve the references the application expects.
Tasks can run manually or on a schedule. Teams review task status and errors, then use the default loader or a database-specific option when the target supports one.
Frequently Asked Questions
What is the difference between masked and synthetic test data?
Masked data starts with selected production records and replaces sensitive values. Synthetic data creates new records instead of copying the original values.
Can one test environment use both approaches?
Yes. Teams can run separate masking and generation tasks for different parts of the environment.
Can test-data tasks run on a schedule?
Yes. Tasks can run manually or on a schedule, with status and errors available for review.
Which loading method should a team use?
The default loader is the simplest starting point. Database-specific loaders are available for supported targets and can improve throughput for larger datasets.
Is this the same as Dynamic Data Masking?
No. Test Data Management prepares data for non-production environments. Dynamic Data Masking changes the result shown during live access without rewriting the stored value.
Map the right workflow