Protected, reliable data for non-production environments
Development, QA and staging need data that behaves like production data. Copying it solves the technical problem and creates a security one.
Four copies of production are four risks
For QA, DevOps and data architecture teams.
Fewer controls than production
Development environments usually have more users with access, less auditing and shared credentials.
Data that doesn't work
Masking scripts break foreign keys and the team gets a dataset that doesn't reproduce real behavior.
Weeks of waiting
Requesting test data is a ticket that depends on one person, and releases are delayed because of it.
Unnecessary volume
A QA environment rarely needs the full copy of production, but it gets it because a coherent subset takes effort.
Data arrives when the pipeline requests it
Know what you have before copying anything
Every field with personal information is identified, with its risk level.
Transform without breaking the model
Data is replaced with realistic data and the relationships between tables are preserved, so tests keep working.
Deliver only what's needed
Subsets tailored to each environment, delivered on a schedule or with every deployment.
Questions from QA and DevOps teams
How is this different from a masking script?
A script transforms columns in isolation, so foreign keys stop matching. With a dictionary, Gigantics gives the same substitute to the same value across every table, and the dataset keeps reproducing the model's behavior.
Does my QA team need access to production?
No. The transformed dataset is delivered to the destination environment from the platform, so people working in development, QA or staging don't need credentials for the source.
Can I generate environments smaller than production?
Yes. You can limit the volume by number of records or percentage and filter by conditions, so each environment gets only what it needs.
Stop copying production to your test environments
We show you the full cycle on an architecture like yours.