Design Transformation Maps for iSeries V7R1 (DB2/400)
Last update March 28, 2016
DB2/400 users who have access to the CA Test Data Manager Datamaker component are also able to create and design the transformation maps for Fast Data Masker within the Datamaker UI. Once a map is base lined in Datamaker, it can be moved to iSeries server using an FTP client, System i Navigator, or mapped network drive.
Using Datamaker to design your transformation maps has the following benefits:
- Datamaker stores the transformation maps in its Oracle repository, providing long-term storage.
- Datamaker lets you version control your transformation maps.
- Datamaker offers data sampling, which enables you to sample table columns to find any personally identifiable information (PII) you need to mask.
- Datamaker lets you build and store a data model of tables in the project.
Follow the steps mentioned in this article to understand how to use the Datamaker UI to create transformation maps: You can use those transformation maps in Fast Data Masker for masking purpose:
- Access the Datamaker UI and open the Maintain Projects dialog.
- Right-click the root of the projects tree and select New Project and Version from the context menu.
- Enter a project name, version, and click the Advanced options icon.
- Edit and set the following three project attributes as a best practice:
- File Publish DBMS : db2/400
- Publish to: Data Target
- Key order of data group, set and pool : SEQ
- Click the save icon to complete the project creation process.
Click on the Data Source menu item and confirm that Datamaker is connected to a db2400 database and schema, which has the tables to be masked.
It is important that this source connection is as good as production. This is because Datamaker makes use of the information in the database catalog of this source connection to determine table relationships and sample values of the tables to be masked.
- Right-click the project version and select Register.
Select the Database Table option and click the forward arrow.
Select the Register Tables from Data Source option from the drop-down list.
Select tables to be masked from the source schema and click the forward arrow to register. Once the registration is complete, Datamaker may prompt you to choose if you want to calculate the table order of the registered tables. Select Yes.
Navigate to the Maintain Projects dialog, right-click the project version, and select the Action for Registered Objects option.
Select the Sample Data option from the drop-down list and select tables to be sampled.
Click the forward arrow to start the sampling process.
- Select the Sample from Source Connection option and also ensure that you select the limited data sampling functionality by choosing a maximum number of rows to be sampled.
db2400 does not have an inbuilt support for data sampling, so using ‘limited data sampling’ of Datamaker is the quickest and best way to gain information about the tables to be masked.
This action opens up the data sampling results dialog, which provides data sampling information about every column in the first x number of rows (for example, 100) in the table. This information is auto-saved by Datamaker and is associated with the given project and version. This information is more useful when viewed in Transformation Maps.
- Navigate to the Maintain Projects dialog and highlight the project version in the Context field to ensure that Datamaker is aware of the correct context.
- Click Projects, Transformation Maps.
- Open a list of existing transformation maps and create a new Fast Data Masker map for the given project and version. Ensure that you choose map DBMS as SDM.
- In the Transformation Map dialog, you can review the following information:
Datamaker automatically discovers the data type of the given column and suggests a list of data masking function for it. Transformation sheets are saved into the Oracle repository of Datamaker using the blue save icon. Transformation sheets are exported to CSV file using the grey save icon. Once exported, you can use them in Fast Data Masker.
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