How to Mask a Database using Test Data Automation

Test Data Automation provides a quick and intuitive approach to masking complex test data consistently. Find out how in this short data masking tutorial.

Compliance and speed should not be a choice

Data masking is a common compliance requirement, needed to anonymize data before it is moved to less secure test environments. However, test data masking can slow, complex and laborious. It requires deep understanding of complex data models, consistently masking values from numerous different data sources. Masking manually or using in-house tools and scripts often then presents a bottleneck, leading to lengthy delays before data is provisioned to test environments. It is also error prone, and further delays emerge as tests fail due to invalid or inconsistent data sets. Delivering quality software at speed instead requires a rapid and reliable approach to data masking.

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Compliant data at speed

Test Data Automation removes the bottlenecks from test data masking, providing a quick and simple approach to masking interrelated data. Masking is driven by easy-to-configure spreadsheets, auto-populated based on the source data to create a “fill-in-the-blanks” approach. Data scanning identifies potentially sensitive data, suggesting methods to mask problematic columns. Defining masks for complex data is then as quick and simple as choosing from combinable data masking functions, using hashing algorithms to maintain the referential integrity of data. The high-performance, parallelized masks can be performed by the data provisioning team or can be embedded into automated test execution and DevOps toolchains. The result? Compliant data to drive rapid and rigorous testing.

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A simple process for masking complex test data

Watch this short data masking tutorial, masking an E-Commerce SQLServer database, and understand how:

  1. Easy-to-configure Excel spreadsheets provide an intuitive, fill-in-the-gaps approach to masking complex data consistently.
  2. Automated data scanning identifies sensitive data that needs masking, providing recommended masking functions for potentially problematic columns.
  3. Masking sensitive data is as quick as picking from a complete set of Excel-like functions, using hashing algorithms to retain the referential integrity needed in testing.
  4. High performance, parallelized masking can be run from a collaborative web portal and from the command line, or as part of automated test execution and within DevOps toolchains.
  5. Optional auditing tracks differences made to data, validating that defined masks anonymize data reliably and accurately.
  6. Masking can be performed in flight or in place, incorporating custom functions if needed.
  7. Data masking can be integrated seamlessly with a full range of TDM utilities, including synthetic test data generation, subsetting, and data allocation.

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