Masking, subsetting, and synthesis for NoSQL databases

Yes, a quality test data platform for semi-structured data does exist


All the capabilities you need to generate realistic NoSQL data from real data for streamlined development and testing.
Artificial structure
Automatically handle various data types within the same field across documents via an artificial hybrid document.
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Mask different data types using different rules, even within the same field.
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Generate referentially intact subsets of your document-based data sized to your developers’ needs.
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The value of synthetic NoSQL data
Developer productivity

Equip development, testing, and QA with quality NoSQL data to speed up your release cycles and get your products to market faster.

Faster time-to-market

Enable shift-left testing and development with realistic NoSQL data for earlier bug detection and product optimization.

Cost savings

Reduce infrastructure costs and eliminate resource-heavy workarounds by streamlining NoSQL test data management.


Reduce risk in your engineering org and ensure regulatory compliance by enforcing security policies within fake data generation.

Global enablement

Unblock your off-shore resources by equipping them with synthetic test data that is safe, useful, and accessible across borders.

Essential NoSQL test data applications

For testing and QA
Generate synthetic NoSQL data that mirrors production to fix your staging environments, catch more bugs, and shorten release cycles.
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For local development
Get smaller, targeted document-based datasets, de-identified on demand, to maximize efficiency and minimize risk.
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Tonic integrates with leading NoSQL solutions

Connect natively and generate realistic test data in MongoDB, DocumentDB, and JSON files.
Logo cloud of data source integrations for Tonic
“Tonic has an intuitive, powerful platform for generating realistic, safe data for development and testing.”
Senthil Padmanabhan
Senthil Padmanabhan
Technical Fellow, VP of Engineering
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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.
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