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Transformation Rules

The transformation term describes a manual intervention which is required to modify the data into required format after extracting the source data.
  1. Active Transformation
    The output record count of the transformation may or may not equal to input record count.
    For example, when we apply filter transformation for age column with the condition of age between 25 and 40. In this case, the data will come out which satisfies this condition, hence the outcome count cannot be predicted.
  2. Passive Transformation
    The output record count of the transformation is equal to input record count.
    For example, when we apply expression transformation to concatenate first name and last name columns, in this case, the data will come out even though the columns do not have values.
  3. Connected Transformation
    A transformation which is being linked with other transformation or target component is called connected.
  4. Unconnected Transformation
    A transformation which is not being linked with any other transformation or target component is called unconnected.
List of transformations based on Informatica power center tool,
  1. Joiner
It’s an active transformation. It joins 2 or more sources along with join condition. The data will be returned if it satisfies the join condition else data will be rejected.
  1. Filter
It’s an active transformation. A column can be selected as a filter with a condition. The data will be returned if it satisfies the filter condition else data will be rejected.
  1. Expression
It’s a passive transformation. An expression can be mentioned like concatenation or replacement for NULL values. The expression will be applied to a specific column and returned.
  1. Sorter
It’s an active transformation. The sorting column can be selected along with the order to be sorted either ascending or descending. Based on the column and order the rows will be sorted.
  1. Aggregator
It’s an active transformation. An aggregate function can be applied to a measure such as Max, Avg, Max, count and Min etc.
  1. Router
It’s an active transformation. The routing condition can be mentioned and the data will be routed/divided based on condition, different functionality will be applied on divided data.
For example, a source table has active_flag column which contains values as YES or NO. The requirement would be loaded the data directly for active_flag=YES else update is_active=D then load.
  1. Union
It’s an active transformation. The two or more sources can be merged with this transformation.
  1. Lookup
It’s an active transformation. The lookup table and condition to be mentioned, if condition satisfies the required column value of lookup table will be returned.
  1. Rank
The rank number will be generated based on given Grouping column and order.
  1. Normalizer
It’s an active transformation. It converts the normal flat file into relational database data format.
  1. Sequence Generator
It’s a passive transformation. It generates a sequence number for the specified column. Mostly it will be used for creating a surrogate key.
  1. Stored Procedure
It’s an active transformation.  A stored procedure can be invoked during workflow run.
  1. Source Qualifier
It’s a passive transformation. It converts the data types into Informatica specific data types.
  1. Transaction Control
It’s an active transformation. It controls the commit and rollback of transactions.
  1. Java Transformation
It’s an active transformation. Java code will be invoked during workflow run.

1 comment:

  1. I like this article because this is very helpful for me.

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