About this software
Data Masker for SQL Server anonymizes sensitive data in Microsoft SQL Server databases to enable safe development, testing, and analytics using realistic datasets. It applies configurable masking rules including substitution, shuffling, redaction, nulling, and encryption while preserving referential integrity and data formats. Masking processes can be scheduled or automated via a command-line interface, and the product provides logging and reporting for auditability.
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Redgate Data Masker for SQL Server
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€11,694.19
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Benefits
- Mask sensitive data: Replace, shuffle, or null sensitive values across databases
- Preserve referential integrity: Maintain key relationships while masking related tables and columns
- Multiple masking algorithms: Choose from substitution, format-preserving, and encryption methods
- Automate masking tasks: Schedule jobs or run masking from the command line for automation
- Audit and reporting: Generate logs and reports to track applied masking operations
Available languages
- English
Support information
- Product documentation: Comprehensive user guides and reference documentation are available on Redgate's documentation site
- Technical knowledge base: Searchable articles and troubleshooting notes are provided through the publisher's support portal
- Community forum: Users can discuss issues and share techniques on Redgate's community forums and Q&A
- Command-line support: Masking operations can be invoked from the command line for automation and scripting
- Scheduling and automation: Built-in scheduling lets teams run recurring masking tasks without manual intervention
Frequently asked questions
What is Redgate Data Masker for SQL Server used for?
A data-masking solution for SQL Server that anonymizes or obfuscates sensitive data in databases to reduce exposure in non-production environments while retaining data format for testing and development.
How does it preserve referential integrity across related tables?
By applying consistent masking rules and lookup-based substitutions across related columns, it maintains relationships so masked data remains referentially consistent for realistic testing.
What types of data can be masked?
Commonly supports strings, numbers, dates, identifiers, and structured sensitive fields such as personal information and payment identifiers, typically using format-preserving transformations.
Can masking be automated and included in CI/CD or deployment workflows?
It can be executed through scripted tasks or scheduled jobs to create masked database copies as part of deployment or test-data refresh workflows for repeatable anonymization.