Table of Contents

Search

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

Transformations

Transformations

Access Policy transformation example

Access Policy transformation example

You need to make customer data available to a colleague in your bank's marketing department for customer analysis. As part of their role and the needs of their project, your colleague in the marketing department has limited access rights to customer data.
Use an Access Policy transformation to ensure that your organization complies with data privacy regulations while allowing your colleague to gain valuable insights. At run time, the transformation hides or obscures the information for which your colleague does not have access rights based on metadata and the properties that you set on the Access Policy tab.
The following image shows part of a table called BANK_CUSTOMERS, which includes contact details, birth dates, and other items of personally identifiable information:
 The image shows a database table that includes bank customer information, including customer IDs, names, birthdates, and contact information.
To configure the mapping, complete the following tasks:
  1. Add a Source transformation that reads the BANK_CUSTOMERS source table.
  2. Add an Access Policy transformation to the mapping canvas, and connect it to the data flow.
  3. On the
    Access Policy
    tab, take the following actions:
    1. In the
      Consumer
      field, select your marketing department colleague.
    2. In the
      Usage
      field, select "customer analysis" as the usage context.
      Users create and manage usage context in
      Data Marketplace
      . To learn more, see
      Working With Data Collections
      in the Data Marketplace help.
    3. In the
      Data Asset
      field, select the BANK_CUSTOMERS table as the source data asset.
    4. To use consistent tokenization, enter a consistency seed from another Access Policy transformation. Otherwise, generate a new consistency seed.
    5. Click
      Synchronize Access Policy
      .
  4. Add other transformations to the mapping as required.
  5. Add a Target transformation to the mapping and connect it to the upstream transformation.
  6. Click
    Run
    .
    The
    mapping
    task applies the policies to the data, protecting it.
The following image shows the protected data:
 The image shows a database table that includes bank customer information, but the customer IDs, customer names, birthdates, and contact information have been modified to make it difficult to identify individuals.
The Access Policy transformation protects the data in the following ways:
  • It retains only the first four characters of the customer names.
  • It replaces the street address data with null values.
  • It assigns random characters to the customer email addresses, but it keeps the addresses in a valid email format.
  • It replaces genders with the same six-character string for each gender.
The ZIP codes, countries, and professions remain unaltered.

0 COMMENTS

We’d like to hear from you!