New Features and Enhancements for Version 9.7.1
This section describes the new features and enhancements for version 9.7.1.
XML Sources
You can perform data subset, data masking, and data generation operations on XML sources.
Import the source definition from the PowerCenter repository. Apply subset criteria, data masking rules, or data generation rules to perform the required operation.
For information about how to perform TDM operations on XML sources, see the Test Data Management 9.7.1 User Guide.
Session and Workflow Logs
You can configure the workflow and session log properties when you create a plan.
Edit the Log Settings section when you configure a plan. Choose to create workflow logs by runs or by time stamp. Choose to create a log file in addition to binary logs for log events. Configure properties for session and partial session log files.
For information about how to configure session and workflow logs in a plan, see the Test Data Management 9.7.1 User Guide.
Naming Convention for Workflows, Mappings, and Sessions
You can customize the naming convention for workflows, mappings, and sessions that you create in TDM.
Create a naming format to standardize the names of workflows, mappings, and sessions. When you customize the naming format, all workflows, mappings, and sessions that TDM creates use the same format.
For information about how to customize the naming format, see the Test Data Management 9.7.1 Administrator Guide.
Import Data Domains From the Model Repository
You can import data domains from the Model repository into the TDM repository.
The TDM repository must contain data domains listed in profiles that you import from the Model repository. Import the data domains from the Model repository before you import the profile into TDM.
For information about how to import a data domain from Test Data Manager, see the Test Data Management 9.7.1 User Guide.
Seed Parameterization
You can enter the seed value as a parameter when you create a masking rule that requires repeatable output.
Add a variable for the seed value to the parameter file and enter the variable when you create the masking rule. If you require a different seed value, you can edit the seed value in the parameter file and then run the plan again. You do not need to edit the masking rule.
For information about how to use seed values in data masking rules, see the Test Data Management 9.7.1 User Guide.
Change Propagation
When you update or delete policies, data domains, masking rules, generation rules, and data subset components, a warning message appears with the list of affected objects that contain assignments.
You can either choose to cancel the updates or continue with the changes. You can download the list of affected objects and save the .csv file.
If you update a rule, you change the properties within the rule. The changes do not take effect in the plans that contain the rules. You must fix the changes in the plan, and generate and run the plan again. If you make changes in the name or type of the field of a custom rule, the assignments do not contain the changes. You must import the mapplet again.
For information about how to use seed values in data masking rules, see the Test Data Management 9.7.1 User Guide.
JDBC Connection
You can use the connection type JDBC to create a JDBC target connection.
Use this connection when your target connection is a MySQL database and you want to disable and enable constraints in a plan. You need to select a target JDBC connection. You can use the JDBC connection type to create a connection with a MYSQL database.
Hadoop Data Sources
You can enable Kerberos authentication for Hadoop data sources.
You can use auto cascades and value cascades for Hadoop data sources.
You can apply shuffle, substitution, mapplet, and advanced masking rules to mask Hadoop data sources. You cannot use advanced email masking and unique substitution rules to mask Hadoop data.
For information about the data masking techniques, see the Test Data Management 9.7.1 User Guide.
Flat Files
You can configure the flat file properties such as file encoding, file format, optional quotes, and include headers.
If the source is relational, you can specify the file encoding type for the target flat file. If the source is a flat file, the default file encoding type of the target flat file is the same as that of the source flat file. You can choose the fixed width or delimited format for a flat file.
You can select an optional quote to separate columns in a .csv file based on the data. You can select single quote, double quotes, or no quotes. You can choose to include headers in the target flat file.
For information about the flat file properties, see the Test Data Management 9.7.1 User Guide.
Data Generation Rules
The data generation rules include the following enhancements:
- Decimal Values in Percentages
- In a data generation rule, you can enter decimal values up to two places in distribution percentages for data patterns, list of values, null values, and values that are not valid.
- Generate Numbers in a String Column
- In an ad hoc generation rule, you can generate numbers in a column with string data type. To generate numbers in the column, select Override Data Type to Numeric when you create an ad hoc generation rule.
- Start Sequence from Last Value
- In a sequence generation rule, you can choose to start the data sequence from the last output value. When you run a plan again, TDM generates the sequence of data from the previous output value. You can choose to start the sequence from the last value when you want to generate primary and unique keys.
For information about the data masking techniques, see the Test Data Management 9.7.1 User Guide.
Add Data Generation Rules to a Data Domain
Assign one or more data generation rules to a data domain. When you assign a generation rule to a data domain, the columns in the domain receive the data generation rule when you configure data generation.
When you add data generation rules to a data domain, you can enable one of the rules to be the default rule. The default rule is applicable to all columns in the data domain.
For information about how to add data generation rules to a data domain, see the Test Data Management 9.7.1 User Guide.
Column Profile
Create and run a column profile to determine the data source column characteristics, such as a list of values, data patterns, and value ranges.
When you create a column profile, you can select the columns on which you want to run a profile, and configure data sampling options.
After you configure and run a column profile, TDM analyzes column data and infers rules based on the data type. Column data types include all the inferred data types for each column in the profile results. Use the column profile results to generate test data.
Auto rules are the inferred rules that you assign after you run a column profile. When the status of inferred rule is Yes, you can assign the auto rule to target columns to generate test data.
For information about how to create a column profile, see the Test Data Management 9.7.1 User Guide.
Unique Key Constraints
Add a column or a set of columns as a constraint to define a unique key in a table.
A unique key column can contain null values. You can create more than one unique key in table. You can perform multiple data generation assignments when you filter the columns based on unique keys.
For information about how to create a unique key constraint, see the Test Data Management 9.7.1 User Guide.
Default Settings
Use default settings to generate test data for the columns that you have not assigned specific generation rules to.
When you create a project and import target metadata, the project contains the default generation rules for all data types that a TDM administrator configures. In a project, you can view the default data generation rules, default primary key generation rules, and default unique key generation rules for all data types.
For information about how to configure default settings to generate test data, see the Test Data Management 9.7.1 User Guide.