Published Release Notes

Easier customer record management in Customer Profile Designer (early preview)

The new Customer Profile Designer module of Pega Customer Decision Hub™ makes it possible for marketing analysts and strategy designers to define the associated data for each customer context directly in the Pega Customer Decision Hub portal. It is also possible to define more complex associated data structures that use a custom data flow, or define associated data of different types, such as RDBMS and Cassandra, for the same customer context.

Customer Profile Designer is available in Pega Customer Decision Hub 8.6 version as an early preview version. The functionality will be further expanded in future releases.

For more information, see Manage customer records in Customer Profile Designer (8.6).

Text predictions simplify the configuration of text analytics for conversational channels

Enable text analytics for your conversational channels, such as email and chatbot, by configuring text predictions that manage the text models for your channels. This new type of prediction in Prediction Studio consolidates the AI for analyzing the messages in your conversational channels in one place and replaces the text analyzer rule in Dev Studio.

Through text predictions, you can efficiently configure the outcomes that you want to predict by analyzing the text in your channels:

• Topics (ticket booking, subscription cancellation, support request)
• Sentiments (positive, neutral, negative)
• Entities (people, organizations, airport codes)
• Languages

You can train and build the models that predict these outcomes through an intuitive process, and then monitor the outcomes through user-friendly charts.

For more information, see Predict customer needs and behaviors by using text predictions in your conversational channels.

Upgrade impact

Channels that you configured with text analyzers in the previous version of your system continue to work in the same manner after the upgrade to the current version. When you edit and save the configuration of an existing channel, the text analyzer rule is automatically upgraded to a text prediction. The associated text prediction is now an object where you can manage and monitor the text analytics for your channel. When you create a new channel in the upgraded system, the system automatically creates a text prediction for that channel.

What steps are required to update the application to be compatible with this change?

1. Enable the asynchronous model building and reporting in text predictions through job schedulers that use the System Runtime Context (SRC) by adding your application to the SRC.
   For more information, see Automating the runtime context management of background processes.
2. Enable model building in text predictions by configuring background processing nodes.
   For more information, see Assigning decision management node types to Pega Platform nodes.

Upgrading to Hazelcast 4.x requires downtime during upgrades to Pega Infinity 8.6

Upgrade impact statement

On-premises upgrades of Pega Infinity release 8.4.2 and later to version 8.5.1 or later on Tomcat and PostgreSQL are completed with near-zero downtime. However, upgrading to Hazelcast 4.x requires that you shut down and restart your application server.

What is required to update the application to be compatible with this change?

Hazelcast 3.x is enabled by default. For near-zero downtime upgrades, you do not need to perform any action.

For instructions about upgrading to Hazelcast 4.x, see one of the following topics:

• For near-zero downtime upgrades from Pega Infinity release 8.4.2 or later on Tomcat and PostgreSQL, see "Optional: upgrading to Pega Platform version 8.6: Upgrading to Hazelcast 4.x" in Near-zero downtime Upgrade Guide for Pega Platform version 8.4.2 and later for Tomcat and PostgreSQL.
• For all other upgrades, see "Optional: upgrading to Hazelcast 4.x" in the appropriate upgrade guide.

Kafka data set enhancements

The Kafka data set is a high-throughput and low-latency platform for handling real-time data feeds that you can use as input for Pega Platform event strategies.

For better integration of Pega Platform with externally hosted Kafka, the following enhancements are implemented:

• Support for Kafka message keys and headers - extended values data format (JSON Data Transform, Apache Avro)
• Custom value processing
• Configuring topic names by using Application Properties
• Data-Admin-Kafka enhancements - supporting a wide range of connection properties

For more information, see Improve your Kafka data set with new enhancements.

Case archiving enhancements in Pega Cloud Services

To provide a more complete archiving solution to Pega Cloud clients, we have introduced several enhancements to the archival functionality for your Pega Platform database. This includes support for your data retention policy to expunge (permanently delete) archived data from Pega Cloud File Storage.

Permanently delete case data with data retention policy

In previous versions, Pega Cloud clients could archive resolved cases and associated data from the Pega database to Pega Cloud File Storage after the cases have been resolved for a specified number of days with an archival policy. Now, clients can permanently delete archived data from Pega Cloud File Storage after the cases have been resolved for a specified number of days with a data retention policy. 

Faster adoption with testing mode

Clients can now enable a testing mode and specify archival policies in minutes instead of days. Now you create and resolve cases, then run archiving process immediately to test the functionality within minutes.

Easier adoptions with enhanced monitoring capabilities

With the addition of the Log-ArchivalSummary class and its associated log files, clients can monitor their archival jobs in a single view. We have also improved logging for archival jobs, offering you greater insight into the success of your archival process.

To learn more about archiving and purging your case data in your Pega Cloud environment, see Improving performance by archiving cases.

External data flow rules are removed

In previous versions of Pega Platform™, you could configure data flows to run in an external Hadoop environment. The external data flows functionality was deprecated and hidden from view in Pega Platform 8.5. The functionality has been now removed and is no longer available in Pega Platform 8.6.

For more information, see External data flow rules are deprecated.

Improved indexing of StringList and StringGroup property types

Search and Reporting Service in Pega Platform™ 8.5 may improperly index StringList and StringGroup property types. As a result, the data model does not include the affected properties.

Upgrade impact

After upgrading to Pega Platform version 8.6, the system requires that the classes with the StringList or StringGroup type are reindexed. 

What steps are required to update the application to be compatible with this change?

On the Search Landing Page, manually reindex all the classes that include properties with the StringList or StringGroup types to ensure that all your data is present in the data model. Alternatively, if finding specific instances of classes is difficult, you can reindex all classes in your application.

For more information, see Indexing class data.

LDAP Authentication Service URL resolution

The latest Pega Cloud infrastructure update includes Java JDK (JDK 89u181), which contains improvements to LDAP support. This Java JDK enhancement can prevent insecure logins by verifying that the hostname specified in the LDAP URL matches the hostname that you specified in the Trust store certificate in the JNDI Binding Parameters section of the Authentication Service rule. An LDAP Authentication Service can no longer resolve using IP addresses.

This is a one-time fix and does not affect Pega Cloud clients with security-compliant LDAP settings and certificates.

Required client workaround

For clients that previously configured LDAP in their applications running in a Pega Cloud environment using IP addresses, after Pega Cloud Services notifies you that the update is complete, you must edit your LDAP Authentication Service rule form Directory field to use the URL value of the domain name or a machine within the domain that matches the URL used by the SSL certificate in the Trust store. 

For example, if your SSL certificate uses the test.abc.com machine name, enter ldaps://test.abc.com:[portNumber] inthe Authentication Service Directory field.

For more information about creating or editing an LDAP Authentication Service, see Creating a custom authentication service.

Naming pattern changed for file data sets

File data sets are used to import from and export data to a file repository. In case of data export, prior to version 8.6.3, the first file exported had the same file name that was provided by the user in the data set, and any subsequent file exported to the repository had a unique identifier appended to it. Starting in Pega Platform version 8.6.3, each file has a unique identifier, automatically generated based on the data flow node, thread ID, and timestamp.

Upgrade impact

If your process to consume output files expects files with a specific name, it may not be able to process the resulting files correctly.

What steps are required to update the application to be compatible with this change?

If you have configured the process before updating to Pega Platform version 8.6.3, but the exported files are no longer recognized by downstream processing logic after the upgrade, ensure that the downstream tool is configured to recognize the files by a pattern rather than the full name. For example, when referring to files exported to the repository, use the * character to indicate a pattern instead of using the full file name. For example, use Export*.csv to refer to the files.

Facebook and YouTube data sets are deprecated

Pega Platform™ no longer supports the data set types in the Social category:

• Facebook data sets
• YouTube data sets

These features are deprecated and will be removed in future Pega Platform versions. Do not create any data sets using the Facebook or YouTube types.