The most powerful data extraction solution for creating logical data sub-sets while maintaining referential integrity.
Large Test Data Environments slow down testing and make it hard to find the data scenarios you need, and limited disk space on test machines means fewer environments than you would like.
Intelligent Data Extraction enables you to continue to use production data while focusing on the scenarios you want and need for your testing, while maintaining data integrity. A small, focused sub-set of production data means you can find what you are looking for, testing is accelerated, and, consequently, you now have space for more environments.
TestBench analyses the relationship between tables in a data environment to ensure that full referential integrity of the data is maintained when an extract is performed. This ensures a usable, working data environment for testing.
Data selection for an extraction is defined by selection and sampling criteria. While selection is standard, sampling extracts data based on combinations of data values, with two or more data elements being used as the criteria for sampling. Sampling enables the creation of test data environments that are small while ensuring all relevant data scenarios are included.
Want to know more about IBM i Data Extraction and how it could help your business? Why not book a slot with one of our technical advisers.
Solutions to cover every aspect of test data management
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