Connect natively to document-based data in MongoDB to de-identify PII consistently with your data stored in relational databases.
Aggregate elements across documents to realistically anonymize sensitive information while providing a holistic view of all your data in all of its versions.
Schema-less Data Capture
Tonic builds a hybrid document model to capture the complexity of your data and carry it over into your lower environments. Automatically scan your database to create this hybrid document, capturing all edge cases along the way, so you don’t miss a single field or instance of PII.
Granular NoSQL Data Masking
Mask different data types using different rules, even within the same field. Regardless of how varied your unstructured data is, you can apply any combination of our growing list of algorithm-based generators to transform your data according to your specific field-level requirements.
Cross-database support
Achieve consistency in your test data by working with your data across database types. Match input-to-output data generated across your databases from MongoDB to PostgreSQL to Redshift to Oracle.
De-identify non-Mongo NoSQL data too
Use Tonic with Mongo as a NoSQL interface, to de-identify NoSQL data stored in DocumentDB or your own homegrown solutions. By using MongoDB as the go-between, Tonic is able to mask a huge variety of unstructured/NoSQL data.
Coherent subsetting
Generate referentially intact subsets of your document-based data sized to your needs, environments, and simulations. Shrink petabytes of data down to a size that is manageable and easy to share.
Native connections to the leading warehouses and lakehouses
Google Big Query
Start optimizing and monitoring Postgres with pganalyze.
Azure Databricks
Start optimizing and monitoring Postgres with pganalyze.
Apache Spark
Start optimizing and monitoring Postgres with pganalyze.
Data Warehouses
Big data, unlocked.
De-identify PII consistently and subset referential data coherently across database types—no matter the scale.
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