in Access
Policy transformations, use the following guidelines as best practices.
Data Integration
treats both date and
timestamp data types as timestamps. As a best practice, in
Data Access Management
, create a
data filter rule
or a
cell-level
de-identification
with two
distinct criteria. In one criterion, use the date data type. In the other criterion, use the
timestamp data type with the same values as the first criterion. This second criterion is
for the Access Policy transformation.
In order to provide flexibility for a variety of use cases, the Access Policy transformation
appends a new field called access_policy_filter that indicates whether a row is affected by
data filter policies
. For most use cases, it is appropriate to filter out these rows and the
access_policy_filter field from the output.
Use the following best practices when defining Access Policy transformations that include
data filter policies
:
Add a Filter between the Access Policy transformation and the Target.
On the Incoming Fields tab, include all
fields.
On the Filter tab, add a simple filter condition for the field name
access_policy_filter with a value of ACCESS_DENIED.
The following image shows the
Filter tab:
Select the Target in your mapping.
On the Incoming Fields tab, exclude the field named access_policy_filter.
The following image
shows the Incoming Fields tab for the Target:
Start the name of appended columns with
“cdamx_”.
If you need to pass additional column
information through an Access Policy transformation, you can select “Query” from the
Source Type menu and start the name of the appended columns with “cdamx_”.
For
example, if you want to add the row number to the existing table, you can write a
query to select all columns as they appear in the catalog and append the row number
column as "cdamx_rownum".
ASK INFAPreview
