Published Release Notes

Apply logic to data import fields

When importing data from an external system, you can translate data fields into values that can be read by Pega^® Platform. From the Import wizard, you can do the following actions:

• Apply a decision tree or decision table to the data that is imported for a field.
• Use imported data for a field to look up a value in another record. For example, you can look up the key of a record that uses an external identifier to populate a foreign key field.
• Create templates that contain business logic, which eliminates the need to handle data transformations manually during preprocessing.

For more information, see Transforming data during import.

Expose properties in Decision Data Store

You can now expose properties in Cassandra-based Decision Data Store data sets. With this solution, you can update single properties in extended customer analytic records (XCAR) without having to update the full customer record.

For more information, see XCAR in Decision Data Store.

Enhancements to bulk editing options for unversioned propositions

Bulk editing options for unversioned propositions have been enhanced to allow the use of Excel for editing. You can export and import unversioned propositions into Excel by using the Import wizard. Additionally, data types replace decision tables as the new storage format for unversioned propositions.

For more information, see Data type management and Proposition management enhancements.

Reuse existing propositions across applications

You can reuse existing propositions from built-on applications by using a new wizard to copy proposition groups across applications. This wizard gives you more options and flexibility when you define the business hierarchy in your application.

For more information, see Proposition management enhancements.

Migrate DDS data across various schema versions

You can use multiple versions of a single Decision Data Store (DDS) data set type in different applications that run on the same Pega^® Platform instance and efficiently move data across those versions even if the underlying data schema is different. This solution improves the data management process and simplifies the reuse of existing data.

For more information, see Decision Data Store migration.

Support for PMML version 4.3 in predictive models

You can now leverage predictive models in the Predictive Model Markup Language (PMML) version 4.3 by importing them into predictive models on Pega^® Platform. Pega Platform utilizes a large part of the PMML standard with the exception of some PMML 4.3 features that are not supported in the Predictive Model rule.

For more information, see Support for PMML version 4.3 in predictive models and Creating a predictive model.

Emit remaining event strategy data at the end of a data flow run

Data flows have been updated to prevent data loss in event strategies. By default, when a batch or a real-time data flow stops and it contains an event strategy with Tumbling windows, all of the events that are in the Tumbling windows are emitted. Event emission triggers further processing in the event strategy, like aggregations and joins.

For more information, see Enhancements to data flows that contain event strategies and Event Strategy rule form - Completing the Event Strategy tab.

Ability to map JSON to clipboard properties by using a data transform

You can now map the entire JSON file or individual properties to and from the clipboard by using a data transform. Using a data transform makes it faster and easier to connect your Pega^® Platform to your system of record.

For more information, see Data transforms.

New aggregation functions in event strategies

You can use new functions in event strategies to build standard and complex aggregations. With new aggregation functions you can detect additional patterns in streams of data and deliver actionable insights.

For more information, see Adding aggregations in event strategies.

Continue batch data flow runs that failed and reprocess partitions with the failed records

You can now continue batch data flow runs that failed and complete them with failures. You can also identify all the records that failed during a batch data flow run that is completed with failures. After you fix all the issues that are related to the failed records, you can reprocess the failures by resubmitting the partitions that contain the failed records. This option saves time when your data flow run processes millions of records and you do not want to start the run from the beginning.

For more information, see Data flow run updates.