Rapid and coherent data subsetting

VIP Subset is a high speed test data management utility for creating complete data subsets. Designed with testing and development in mind, VIP Subset rapidly creates referentially intact databases that can be used directly in QA environments.

Manually finding test data undermines agility and rigour.

Testing and developing complex applications quickly requires access to representative, readily available test databases. However, organisations still frequently copy full copies of production databases to QA environments. These are slow to move, and costly data masking often creates even more delays. Parallel test and development teams must then compete for the limited number of copies, and cross-team constraints use up even more time in short iterations. Test and development teams must additionally hunt through the large data sets for the exact data they need, while running the complete databases in automated testing is slow and expensive. 

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Complete test and databases, available in parallel

Subsetting with VIP RPA allows you to provision complete data sets rapidly, driving faster testing and development. Referentially intact data subsets can be built quickly using a simple, automated approach. Testing with isolated data subsets in parallel lifts cross-team constraints, while testers and developers spend less time hunting for the data they need. Smaller data sets are furthermore less resource-intensive to run during QA, and require less complex masking prior to being provisioned to QA environments. The result is less time spent waiting or searching for data, and more time focusing on testing and development. 

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Complete and coherent data subsetting

Watch this short demo of subsetting an SQL Database to see how: 

  1. VIP Subset builds referentially intact subsets with all the interrelated tables needed for a usable test database.
  2. Subset definition is as quick and simple as providing the database connection details. VIP Subset will automatically retrieve all relevant metadata, build a data model, and formulate the subsetting rules needed to retain referential integrity.
  3. High-performance subsets are performed using customisable automated actions, removing the time and complexity of data subsetting.
  4. Subset rules can be refined and re-run rapidly by toggling rules and relationships, quickly working to build the ideal subset.
  5. Data de-duplication creates the smallest possible subset, driving faster, less resource-intensive testing and development.
  6. Data Subsets can be converted easily into usable test databases, adding Primary and Foreign keys automatically post-Subset.
  7. VIP Subset crawls recursively up and down tables, gathering data until a business intact database is created based on pre-defined completion criteria. 

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