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Published Release Notes

Find release notes for the selected Pega Version and Capability

Browse resolved issues for Platform releases.

This documentation is for non-current versions of Pega Platform. For current release notes, go here.

Naming pattern changed for file data sets

Valid from Pega Version 8.6.3

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.

Predictive models monitoring

Valid from Pega Version 8.2

In Prediction Studio, you can now monitor the predictive performance of your models to validate that they make accurate predictions. Based on that information, you can re-create or adjust the models to provide better business results, such as higher accept rates or decreased customer churn.

For more information, see Monitoring predictive models.

Kafka custom serializer

Valid from Pega Version 8.2

In Kafka data sets, you can now create and receive messages in your custom formats, as well as in the default JSON format. To use custom logic and formats for serializing and deserializing ClipboardPage objects, create and implement a Java class. When you create a Kafka data set, you can choose to apply JSON or your custom format that uses a PegaSerde implementation.

For more information, see Creating a Kafka data set and Kafka custom serializer/deserialized implementation.

Additional configuration options for File data sets

Valid from Pega Version 8.2

You can now create File data sets for more advanced scenarios by adding custom Java classes for data encryption and decryption, and by defining a file set in a manifest file.

Additionally, you can improve data management by viewing detailed information in the dedicated meta file for every file that is saved, or by automatically extending the filenames with the creation date and time.

For more information, see Creating a File data set for files on repositories and Requirements for custom stream processing in File data sets.

All search data is encrypted

Valid from Pega Version 8.2

All search data in Pega Cloud deployments is now encrypted, both at rest and in transit. The encryption of search data makes search compliant with regulatory requirements.

For more information about search, see Full-text search.

Authentication service for basic credentials

Valid from Pega Version 8.2

A new type of authentication service is available for authenticating operators by using basic credentials (user ID and password). The default Pega Platform™ login is now an instance of this type of authentication service. All basic credentials authentication services include mobile authentication with the OAuth 2.0 protocol and Proof Key for Code Exchange (PKCE). You no longer have to create a custom authentication service to support mobile applications.

For more information, see Configuring a basic authentication service.

Simplified testing of event strategies

Valid from Pega Version 8.2

Evaluate event strategies by creating test runs. During each run, you can enter a number of sample events with simulated property values, such as the event time, the event key, and so on. By testing a strategy against sample data, you can understand the strategy configuration better and troubleshoot potential issues.

For more information, see Evaluate event strategies through test runs.

Unauthenticated sessions transition seamlessly to authenticated

Valid from Pega Version 8.2

A new authentication service type allows a guest user to use an application without logging in, and to be prompted to authenticate later in the session. This enhancement supports scenarios such as online shopping portals where a user can browse for items and load a shopping cart as a guest but be prompted for credentials at checkout.

For more information, see Configuring an anonymous authentication service.

Data flow life cycle monitoring

Valid from Pega Version 8.2

You can now generate a report from the Run details section of a Data Flow rule that provides information about run events. The report includes reasons for specific events which you can analyze to troubleshoot and debug issues more quickly. You can export the report and share it with others, such as Global Customer Support.

For more information about accessing event details, see Creating a real-time run for data flows and Creating a batch run for data flows.

Data flow runs retry connections that fail

Valid from Pega Version 8.2

Real-time and batch data flow runs now retry dataset connections that fail when the related service is temporarily unavailable, for example, when a connection to a Cassandra database times out. With the automatic retries, you no longer need to run data-heavy and CPU-intensive jobs multiple times and the maintenance of data flow runs diminishes significantly.

For more information about accessing event details, see Creating a real-time run for data flows and Creating a batch run for data flows.

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