what is different between Data Propagation and Data transform?
As I understand Data Propagation auto copy data from parent to child case and data transform copy data from one place to other place regardless same case or not. Am i correct? When should we use data transform?
For the most part you are correct. Data propagation is most often referenced when discussing the creation of child/sub cases which is simply a great way to ensure that each case is instantiated with the appropriate data.
Data transforms are typically much more complex in that there may be any number of actions in a series to get the data in the format required for the ultimate destination.
Here is some additional information/references directly from the Essentials Student Guide:
- Page 184 Architect Essentials contains a lengthy explanation and examples of data transforms and their use cases.
However at the lowest level a data transform defines how to convert data that is in one format and class into data of another format and class.
Often a data transform is a series of actions to complete all of the required transformations to a set of data. When the Pega invokes a data transform, it invokes each action in turn, following the sequence that is defined in the data transform's record form. You can use a data transform to:
- Page 211 Architect Essentials contains a good explanation and example.
Data propagation is the mechanism of copying data within the case hierarchy. By sharing data among cases, you save time and provide relevant information to caseworkers.
Data propagation ensures that the appropriate information is propagated to a subcase. For example, a purchase request case may initiate an inventory selection subcase when units in stock must be confirmed. In the purchase request case, each line item in the purchase request contains a product identifier and a quantity. The inventory selection subcase then uses the product identifier and quantity to verify that the units are in stock.