For some transforms, you can specify patterns to identify conditions or elements of the data on which to take action. These matching patterns can be specified using one of the following types.
|Literal value||An exact string or value.|
The following matches on the exact value between the quotes:
The following matches when two digits appear at the beginning of a value:
Regular expressions are a standard method of describing matching patterns.
The following matches on all numerical values from 0 to 99:
For more information on pattern-based matching, see Text Matching.
Delimiter groups are listed below.
|On delimiter||Transformation is applied based on a specific literal or pattern.|
|Between delimiters||Transformation is applied on database between two literal or pattern-based delimiters. Details are below.|
|On multiple delimiters|
Transformation is applied based on a sequence of delimiters. An individual pattern can be a string literal,
|Between positions||Transformation is applied based on a starting index position and an ending index position. Index positions start from 0 on the left side of any cell value.|
|On positions||Transformation is applied based on a sequence of listed index positions. Index positions start from 0 on the left side of any cell value.|
|At regular interval||Transformation is applied at every nth position. Index positions start from 0 on the left side of any cell value.|
For more information on the underlying syntax for delimiter groups, see Pattern Clause Position Matching.
Each delimiter can either include or exclude the matching value:
|Transform Builder option||Include as part of transform||Include/Exclude|
|Start delimiter||false||Excludes sub-pattern|
|Start delimiter||true||Includes sub-pattern|
|End delimiter||false||Excludes sub-pattern|
|End delimiter||true||Includes sub-pattern|
A condition is an expression that yields a
false value. A condition may include all of the elements of a formula. This value determines whether the transformation is applied to the evaluated row.
A number of transforms support the following parameters.
|D s ordering|
Group parameter: For transforms that aggregate data, such as
window, you can specify the column by which you wish to group the computed aggregations. In the following example, all values in the
POS_Sales column are summed up for each value in the