Provisioning Test Data

As a Test Data Engineer, you are responsible for ensuring quality, coverage level, and referential integrity of application test data. Team access to the right data at the right time is vital to accelerate development velocity and increase quality.  provides capabilities for test data engineers to maintain and provision test data with simple interfaces and automated processes. These capabilities replace processes that previously required manual database scripting.
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As a Test Data Engineer, you are responsible for ensuring quality, coverage level, and referential integrity of application test data. Team access to the right data at the right time is vital to accelerate development velocity and increase quality.
Test Data Manager
provides capabilities for test data engineers to maintain and provision test data with simple interfaces and automated processes. These capabilities replace processes that previously required manual database scripting.
Consider the following high-level sequence of CA TDM test data provisioning activities. Not all environments require all activities:
  1. Discover your data. Connect CA TDM to production (or copies of production) and testing data sources. These sources can include databases, flat files, mainframe files, and other file formats. Connecting data sources with CA TDM helps you represent the data sources in a relational model. This model lets you analyze, manipulate, and optimize data for testing.
  2. Sample and profile your data. CA TDM lets you analyze and sample that data to help you understand required actions. For example, profiling helps you to identify personally identifiable information that requires masking before you use the data for testing.
  3. Subset production data for testing. The volume of production data is often too overwhelming to use in a testing database. You can define rules by which CA TDM can extract a subset of a large data source to use for testing.
  4. Mask production or data subset for testing. Production data typically contains personally identifiable information that you are forbidden to use in testing environments. You can use masking functions to transform all sensitive information into data acceptable for testing.
  5. Visualize test data coverage and identify gaps. Once you have a sanitized set of test data, you can use Data Visualizer to view your overall test coverage and see where you might have gaps.
  6. Generate synthetic data to fill?test coverage gaps. After you understand what data you need to fill coverage gaps, you can use Datamaker to create rules to generate synthetic data based on column data types. You can then publish that synthetic data to any data source for use in testing.
  7. Build a test data repository and configure a data request and reservation system. You can make certain data available for reservation by testers through a portal interface. Test Data on Demand gives testers the assurance that their test data is locked for their use only.
This section describes how you perform all of these operations to provision the right test data to accelerate your testing cycles.