Referentially intact database subsets
Shrink PBs down to GBs without breaking referential integrity, to cut storage costs and maximize developer efficiency
See in action
Features
Create targeted test datasets using custom WHERE clauses or simple percentages to pull in just the data you need from across your database with our patented subsetter.
Subset + mask
Run subsetting and de-identification together to minimize your risk and your data footprint.
Virtual foreign keys
Capture all the relationships in your database by easily adding foreign key constraints.
Patented solution
Explore how Tonic traverses FK dependencies to build your subsets in a video overview of how it works.
The value of database subsetting
- Developer productivity: Enable developers to work more efficiently and effectively with targeted datasets shaped and sized to their needs.
- Cost savings: Drastically reduce storage costs by minimizing your data footprint and eliminating duplicative databases.
- Faster time-to-market: Rapidly deploy isolated datasets to eliminate the friction of shared resources and build faster.
- Data minimization: Minimize the risk of data leaks and breaches by reducing your data footprint across developer laptops.
- Global enablement: Unblock off-shore teams by equipping them with safe-to-share, targeted datasets that respect data localization laws.
Essential subsetting use cases
- For local development: Hydrate your local laptop environments with referentially intact database subsets, to build and debug with ease.
- For off-shore teams: Equip your teams with quality test data wherever they work, subset and de-identified to ensure global compliance.
Tonic integrates with every leading database
Tonic Structural supports leading databases, like MySQL, PostgreSQL, SQL Server, and Oracle, offering seamless synthetic data generation and management for all your development and testing needs.
“Tonic’s subsetter does exactly what I want: it makes the data way smaller, and I won’t have to spend more on storage. It has reduced our costs by over 85%, by not having to use a full image of our database.”
Resources
Explore our guides on all things synthetic test data, from the various approaches of data masking to how to apply test data best practices in implementing test data management software.
Clinical data extraction: how to unlock critical health information
Managing test data from multiple sources without losing consistency
Test data subsetting strategies for targeted software testing
Creating unstructured files from Fabricate Data Agent generated data
Using real-world data for synthetic data generation with the Fabricate Data Agent
Build better and faster with quality test data today.
Unblock data access, turbocharge development, and respect data privacy as a human right.