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  1. Preface
  2. Transformations
  3. Source transformation
  4. Target transformation
  5. Access Policy transformation
  6. Aggregator transformation
  7. B2B transformation
  8. Chunking transformation
  9. Cleanse transformation
  10. Data Masking transformation
  11. Data Services transformation
  12. Deduplicate transformation
  13. Expression transformation
  14. Filter transformation
  15. Hierarchy Builder transformation
  16. Hierarchy Parser transformation
  17. Hierarchy Processor transformation
  18. Input transformation
  19. Java transformation
  20. Java transformation API reference
  21. Joiner transformation
  22. Labeler transformation
  23. Lookup transformation
  24. Machine Learning transformation
  25. Mapplet transformation
  26. Normalizer transformation
  27. Output transformation
  28. Parse transformation
  29. Python transformation
  30. Rank transformation
  31. Router transformation
  32. Rule Specification transformation
  33. Sequence transformation
  34. Sorter transformation
  35. SQL transformation
  36. Structure Parser transformation
  37. Transaction Control transformation
  38. Union transformation
  39. Vector Embedding transformation
  40. Velocity transformation
  41. Verifier transformation
  42. Web Services transformation

Transformations

Transformations

Data access policy best practices

Data access policy
best practices

To effectively utilize
data access policies
in Access Policy transformations, use the following best practices.
  • Data Integration
    treats both date and timestamp data types as timestamps. Create a
    data filter rule
    or a
    cell-level de-identification
    with two distinct criteria in
    Data Access Management
    . 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. Use the second criterion is for the Access Policy transformation.
  • In order to provide flexibility for a variety of use cases, the Access Policy transformation creates a new field called access_policy_filter that indicates whether a row is affected by
    data filter policies
    . In most use cases, you can filter these rows and the access_policy_filter field from the output.
  • When your data source includes additional columns that are not defined in the table, start the name of appended columns with “cdamx_”. If you need to pass additional columns through an Access Policy transformation, you can select “Query” from the Source Type menu and start the name of the additional columns with “cdamx_”.
    The following image shows the Source Type menu on the Source tab:
    In the Source Type menu, you can select "Query" and enter a query.
    For example, to add the row number to the 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."
The access_policy_filter field displays FAILURE_FIELD when
Data Access Management
is unable to apply a
data protection
. The field is redacted with null. This can occur when a field's value does not meet the criteria specified in a
data protection
's regular expression syntax. For example, a
data protection
might consistently randomize a five-digit postal code. If a field contains more than five digits, the access_policy_filter field displays FAILURE_FIELD and the field is redacted with null.
Complete the following tasks when defining Access Policy transformations that include
data access policies
:
  • Add a Filter transformation 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:
      The Filter tab shows "Simple" as the filter condition and ACCESS_DENIED as
                    the value for the access_policy_filter field name.
  • Select the Target transformation in your mapping.
    • On the Incoming Fields tab, exclude access_policy_filter field.
      The following image shows the Incoming Fields tab for the Target transformation:
      The Incoming Fields tab of the Target. The Include operator includes an
                    Include operator and an Exclude operator. The field name is
                    access_policy_filter.
  • When setting Source Type to Query, manually set the Asset parameter.
    • If you select “Query” from the Source Type menu, you must select "Override Asset Name," search for, and select the data asset.
      The following image shows the
      Select Data Asset
      window:
      The Select Data Asset window includes a selection box and an "Override Asset
                    Name" option.

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