Published Release Notes

Automatic retries for the SAVE operation on records

Batch and real-time data flow runs now automatically retry the SAVE operation on records when an error occurs because of resource unavailability. This functionality ensures that the records are eventually saved if the target data set is only temporarily unavailable, for example, because of load or network issues.

You can configure the default number of retries for the entire system in a dynamic system setting. To adjust the setting to different resource allocations and operating environments, you can update that number for each data flow run.

For more information, see Changing the number of retries for SAVE operations, Creating a batch run for data flows, and Creating a real-time run for data flows.

REST API for monitoring DSM services

Decision Strategy Manager (DSM) now provides a REST API that you can use to monitor DSM services without having to access Pega Platform™. By integrating your daily monitoring tools with the management REST API, you can retrieve a list of DSM nodes, check the status of these nodes, and view details of a service from all nodes in the cluster.

For more information, see Getting started with Pega API for Pega Platform.

Create custom criteria for proposition filters by using the condition builder

Proposition filters now use the condition builder to define the criteria that a proposition or group of propositions must match in order to be offered to a customer. The condition builder provides a simple, flexible tool for selecting and grouping the entry criteria.

For more information, see Create custom criteria for Proposition Filter rules with the condition builder (8.3).

Fast-track change requests for high-priority business needs

Perform urgent rule updates through fast-track change requests. The new type of change requests supports addressing high-priority business needs and deploying them immediately, without the need to disrupt an ongoing revision.

For more information, see Release urgent business rule updates through fast track change requests (8.3) and Creating application overlays.

Support for custom database tables in external Cassandra clusters

Pega Platform™ now supports a connection to external Cassandra clusters through a dedicated Database Table data set, which reduces the need for data ingestion and export. You can use custom tables that you store in your external Cassandra cluster in data flows for accessing and saving data. You can access your custom data model by mapping the model to a Pega Platform class.

For more information, see Connecting to an external Cassandra database through a Database Table data set.

Prediction Studio notifications

Prediction Studio now displays notifications when adaptive or predictive models encounter problems. For example, if the performance of a model decreases suddenly, a notification appears, which you can use to immediately investigate the cause of the problem.

For more information, see Accessing Prediction Studio notifications and Prediction Studio notification types.

Production data sampling and migration for decision strategy simulations

Pega Platform™ now supports the sampling and migration of data from the production environment to the simulation environment. The data that is migrated for simulations includes customer details, Adaptive Decision Management information, and interaction history. By running a simulation on that sample data in Pega Marketing™ or Pega Customer Decision Hub™, you can verify how the changes to your decision logic impact the results. You can use that information to optimize and adjust your decision algorithms and processes.

For more information, see Deploying sample production data to a simulation environment for testing.

Integrate text analytics with decision strategies through the Interaction API

The Interaction API provides more context for making next-best-action decisions by integrating text analytics with decision management components such as strategies, propositions, and interaction history. By including natural language processing in your decisioning solution through the Interaction API, you ensure that the next-best-action decisions that you make are more informed and accurate.

For more information, see Customizable Interaction API for text analytics.

Support for machine learning as a service models

In Pega Platform™, you can now run advanced machine learning and artificial intelligence models that you develop in third-party tools. By configuring a connection with an external machine learning as a service provider, such as Google AI Platform, you can use the predictive power of your custom models to improve the predictions in your customer strategies.

For more information, see Connecting to a Machine Learning as a Service model and Configuring a machine learning service connection.

Stream-based alternative for interaction history

Accelerate the processing of high-volume interaction history data by incorporating a stream-based interaction history into your application. A stream-based interaction history processes large volumes of data more quickly than a traditional relational database interaction history so that you can react to your customers' needs in real time.

For more information, see Process high-volume interactions more efficiently and Aggregates-only mode for a stream-based interaction history.