Published Release Notes

Case-insensitive filtering in reports

Filtering in reports is now case-insensitive, improving the reporting and searching experience. You can turn off case-insensitive filtering, for example, by using an activity. You might want to do this if your index is too big or if the length of time it takes for indexing impacts performance.

For more information on report filtering, see Editing filter conditions.

Predictive models monitoring

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

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

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

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

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

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

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

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

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.