Table of Contents

Search

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

Transformations

Transformations

Filter conditions

Filter conditions

The filter condition is an expression that returns TRUE or FALSE.
You can create one or more simple filter conditions. A simple filter condition includes a field name, operator, and value. For example, Sales > 0 retains rows where all sales values are greater than zero.
In advanced mode, the filter condition must evaluate to a numeric result.
Filter conditions are case sensitive. You can use the following operators in a simple filter:

    = (equals)

    < (less than)

    > (greater than)

    < = (less than or equal to)

    > = (greater than or equal to)

    ! = (not equals)

When you define more than one simple filter condition, the
mapping
task evaluates the conditions in the order that you specify. The task evaluates the filter conditions using the AND logical operator to join the conditions. The task returns rows that match all of the filter conditions.
You can use an advanced filter condition to define a complex filter condition. When you configure an advanced filter condition, you can incorporate multiple conditions using the AND or OR logical operators. You can use a constant to represent condition results: 0 is the equivalent of FALSE, and any non-zero value is the equivalent of TRUE.
When you change the filter condition type from simple to advanced, the Mapping Designer includes configured simple filter conditions in the advanced filter condition. You can use or delete the simple filter conditions. The conversion does not include parameters.
To filter rows that contain null values, use the ISNULL function to test the value of the field. To filter rows that contain spaces, use IS_SPACES.
For example, if you want to filter out rows that contain a null value in the First_Name field, use the following condition: IIF(ISNULL(First_Name),FALSE,TRUE). The condition states that if the First_Name field is NULL, the return value is FALSE. The
mapping
task discards the row. Otherwise, the row passes through to the next transformation.
In mappings in SQL ELT mode, you need to enter a filter condition that is valid in your target cloud data warehouse.

0 COMMENTS

We’d like to hear from you!