What are the different types of data transforms in Pega?

Types of data transforms  in Pega

In Pega, types of data transforms in pega are used to manipulate and transform data within the application. They provide a way to map, copy, and modify data across different sources and destinations. There are several types of data transformations in Pega, each serving specific purposes.

Types of data transforms  in Pega
Types of data transforms  in Pega


Different types of data transforms in Pega

1.Apply Data Transform:

Specifies, this is the most common type, used to apply a series of steps that map and transform data from one structure to another. It can be used for simple or complex data transformations.

2. Page-New:

This type of data transformation is used to create a new page or a new data structure in memory; thus, it is essential for initializing new data objects.

3. Page-Copy:

This data transformation type copies data from one page to another. It can be used to duplicate data structures or transfer data between different parts of the application.

4. Page-Remove:

Moreover, this type is used to delete or remove a page from memory. It helps manage memory usage by clearing out unnecessary data structures.

5. Property-Set:

Specifies, this data transform type assigns a value to a property. It is commonly used to assign values to fields or variables in the application.

6. Append and map to:

This type appends a new entry to a list however, maps data to the new entry. It is useful for working with lists and repeating structures.

7. For Each Page In:

This data transform type iterates over each page in a list or group  however, performs actions on each page. It is used for batch processing of data structures.

8. When:

Similarly,this type allows conditional execution of data transformation steps based on specified conditions. It is similar to an if-then-else logic.

9. Set:

Furthermore, It sets property or parameter values within the data transform, thereby enabling dynamic assignment and computation, thereby enhancing flexibility in data processing workflows.

10. Loop:

This data transform type iterates over a collection or list of items. However, performs specified actions on each item, It is useful for processing repetitive data structures.

11. Call Data Transform:

This type allows invoking another data transform from within a data transform. It enables modular and reusable data transformations.

12. Evaluate Data Transform:

Establishes, this type evaluates another data transform conditionally, enabling dynamic execution based on runtime conditions, thereby enhancing flexibility in data processing workflows.

 This rule can be used to perform an assignment operation, i.e., we can assign

  1. Single-value properties
  2. Update the source page to the target page.
  3. Update the source page list to match the match the target page list.

We can call a data transform from another DTF by signing the option “Apply Data Transform.”

Types of data transforms  in Pega
Types of data transforms  in Pega
It can be created under the data model category.
  1. On click or on change of controls.
  2. Data transform
  3. Activity (Using the method Apply-Data Transform)
  4. Flow action under the action tab
  5. Data Page.

PRPC provides an OOTB DTF named ‘pyDefault’ that is called under the process tab of every flow, ensuring consistent execution across workflows.

This pydefault Data Form is responsible for setting default values to properties before the actual WO gets created. When we run a flow, the first pyDefault DTF will get executed, and then the case gets created.

In pyDefault  DTF, we can set Work id prefix to the OOTB property pyWorkIDPrefix.

Work ID prefix can be set at two places

1) Application Rule form         2) pyDefault DTF

The order of assignment of pyWorkIDPrefix is :
  1. Execute pyDefaultDTF
  2. Set pyWorkIDPrefix=C-
  3. Look up application rule form if prefix is Defined or not
  4. If Defined,
  5. Override byWorkID Prefix = prefixDefined in app rule form
  6. Else,
  7. Do not override pyWorkIDPrefix = pyWorkidPrefix.
  • The pyDefault DTF is available in OOTB classes and rule sets like Work-Cover, however Pega-ProCom.  OOTB (Out-of-the-Box) rulesets remain permanently locked.
  • Consequently, if we want to modify any OOTB rules, we have to save them as rules within the same user-defined rule sets and modify them.
  • Moreover, if a rule exists in both the OOTB and User Application rule sets, PRPC always selects the rule from the user-defined rule set over the OOTB rule set.


Overall, these type of data transforms types in Pega provide a flexible. However, powerful way to handle data manipulation, ensuring that data can be effectively and efficiently managed throughout the application.

FAQ section

1. How does the Page-Copy data transform type work, and when would you use it?

  • The Page-Copy types of data transforms in Pega duplicates assigns all properties however, values from a source page to a target page, including nested structures, and overwrites existing data on the target page.
  • Use it when you need an exact replica of a data structure, such as when duplicating a customer record for further processing or creating a backup of data before manipulation.

2. What is a data transform in Pega, and what is its primary purpose?

A data transform in Pega maps, copies, and manipulates data within the application. Additionally, it primarily transforms data between different formats or structures, thereby ensuring seamless data flow and consistency across the entire application.

3. How are the Append and Map to Data Transform types used in handling lists?

  • The Append and Map to Data Transform in Pega adds a new entry to a list and maps specified data. Consequently, it ensures comprehensive and efficient list management.
  • It handles lists by dynamically creating and populating list elements during data processing.

4. How does the Evaluate Data Transform type enable dynamic execution of data transforms?

  • The Evaluate Data Transform type in Pega enables flexible however, adaptive data processing by allowing the conditional execution of data transforms based on runtime conditions. Consequently, this enhances the system’s ability to respond to varying data scenarios effectively.
  • When a specified condition is met, it dynamically selects and runs a data transform, enabling flexible and adaptive data processing

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