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3T Software Labs launches Data Masking and Reschema in Studio 3T

January 2021 by Marc Jacob

3T Software Labs, the independent MongoDB tools company, announced the addition of new security and management tools to the company’s flagship Studio 3T client for MongoDB. Alongside Data Masking for data security, 3T Software Labs has added Reschema to make performance tuning in MongoDB easier over time.

Security and data masking

For security, the addition of Data Masking makes it easier to control how data is presented to those that might use data held within a database instance. By masking data for those that do not need full access, companies can reduce the potential impact from a breach while helping staff members be productive using that data over time.

The new Data Masking tool in Studio 3T means that PII (Personally Identifiable Information) and other types of sensitive data can now be anonymized and shared company wide. DBAs and CDOs can rest easy in the knowledge that if the masked database gets left on a subway bench, it will not compromise the original data from which it was derived.

The business cost of data breaches has been climbing steadily. For example, IBM and the Ponemon Institute released the 14th annual report on the Cost of Data Breaches in 2020 finding that the average cost to business of a data breach in the United States has almost tripled from $3.54 million in 2006, to $8.64 million in 2020. Having the right data management and security tools can help teams reduce potential costs, even if database instances are accessed without authorisation.

Reschema support

MongoDB is known for being fast to set up and easy for developers to work with. However, applications change and develop over time as new functionality is added. The new Reschema function in Studio 3T makes it simple to refactor higher-performance schemas over time, letting developers focus on building new services rather than having to re-architect database deployments every time.

Reschema for MongoDB now supports the following functions:

• Cleaning up MongoDB migrations with simple restructuring of existing tables
• Merging multiple sources and data collections into one source collection, letting users look at data without having to write lookups
• Updating schema designs to reflect changes in application design or expansions in functionality

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