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  1. Preface
  2. Introduction to Test Data Management
  3. Test Data Manager
  4. Projects
  5. Policies
  6. Data Discovery
  7. Creating a Data Subset
  8. Performing a Data Masking Operation
  9. Data Masking Techniques and Parameters
  10. Plans and Workflows
  11. Monitor
  12. Reports
  13. ilmcmd
  14. Data Type Reference
  15. Data Type Reference for Hadoop

Test Data Management Overview

Test Data Management Overview

Test Data Management (TDM) integrates with Informatica applications to manage nonproduction data in an organization.
With TDM, an organization can create a smaller copy of the production data and mask the sensitive data. An organization can discover the sensitive columns in the test data, and ensure that the sensitive columns are masked in the test data.
Organizations create multiple copies of application data to use for testing and development. Organizations often maintain strict controls on production systems, but data security in nonproduction systems is not as secure. An organization must maintain knowledge of the sensitive columns in the production data and ensure that sensitive data does not appear in the test environment. Development must not have to rewrite code to create test data.
Manage data discovery, data subset, and data masking in Test Data Manager.
Data discovery
Use data discovery to run sensitive field profiles to identify the columns that contain sensitive data. Use the profile results to determine which columns to mask and which data masking techniques to apply. Define data domains to identify sensitive data columns by patterns in the data or the column metadata. When you apply data masking, you can apply the same rule to multiple columns in the same data domain.
Data subset
Use data subset to create a small environment for testing and development. You can define the type of data that you want to include in the subset database. You might create a subset database with data based on time, function, or geographic location. For example, a time-based subset database might include recent payment transactions from all invoice data in a production system.
Data masking
Create data masking rules to apply to source columns and data domains. You can apply different masking techniques such as substitution masking, shuffle masking, key masking, and encryption. You can configure repeatable results in the masked data. You can assign multiple rules to the same column.
To perform data subset and masking operations, you can generate and run workflows from data subset and data masking plans in Test Data Manager.
You can perform data masking and data movement on Big Data Edition Hadoop clusters. Use Hadoop sources to lower the cost of raw data storage and to solve large scale analytics by using the distributed computing capabilities of Hadoop. For example, when you move sensitive data into Hadoop, you can classify data for analytics, provision data for testing, or other purposes.
Use Hadoop to improve the speed of processing large volumes of structured and unstructured data. For example, you work with heterogeneous data sets and you want to normalize and correlate data sets of the size of terabytes or petabytes. The analytics results processed on Hadoop are faster and cost-effective, and you can extract the analytics results to a conventional database.
TDM includes the ilmcmd command line program. Run ilmcmd commands to perform a subset of the Test Data Manager tasks from the command line.
TDM users have roles and privileges that determine the tasks that they can perform through Test Data Manager or the ilmcmd command line program. The administrator manages roles and privileges for users from the Informatica Administrator.