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.

Use repositories as sources for File data sets

You can configure remote repositories, such as Amazon S3 or JFrog Artifactory, or a local repository, as data sources for File data sets. By referencing an external repository from a File data set, you enable a parallel load from multiple CSV or JSON files, which removes the need for a relational database for transferring data to Pega Platform™ in the cloud.

For more information, see Creating a File data set record for files on repositories and Configuring a remote repository as a source for a File data set.

Define a taxonomy by using the Prediction Studio interface

Create a topic hierarchy and define keywords for each topic in Prediction Studio faster and more intuitively than by editing a CSV file. If you have already defined a taxonomy in a CSV file, you can import that file and modify existing topics and keywords by using the Prediction Studio interface.

For more information, see Creating-keyword-based topics for discovering keywords and Tutorial: Configuring a topic detection model for discovering keywords.

Improved performance of decision strategies

Strategy rule performance has been improved through the implementation of a new engine. You can perform single and batch test runs to analyze strategy performance, locate and prevent potential issues, and optimize strategy components. Test runs now support data sets and data flows with multiple key properties. The redesigned Test run panel improves the display of information and highlights the most immediately relevant details.

For more information, see Configuring a single case runs and Configuring a batch case runs.

Extract summaries from the analyzed text

You can now configure a Text Analyzer rule to extract information-rich blocks of text from the analyzed content and combine them into a comprehensive and coherent summary. By summarizing large documents, such as emails, you can facilitate making business decisions without having to read an entire document. In Text Analyzer rules, you can combine summarization with other types of text analysis, such as topic or entity detection, to extract the full context from a message.

For more information, see Configuring text extraction analysis and Tutorial: Extracting email context with Text Analyzer rules.

Additional adaptive model predictors based on Interaction History

Customer interactions are now automatically used in adaptive models to predict future customer decisions. For example, a phone purchase registered in Interaction History allows an adaptive model to predict that a customer is more likely to accept supplementary coverage for a new device. Such interactions, collected in a predefined Interaction History summary, are applied as an additional set of predictors in an adaptive model.

The aggregated Interaction History summary predictors are enabled by default for every adaptive model configuration.

For more information, see Enabling Interaction History predictors for existing adaptive models.