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Published Release Notes

Find release notes for the selected Pega Version and Capability

Browse resolved issues for Platform releases.

Support for predictive models in PMML version 4.4

Valid from Pega Version 8.5

Pega Platform™ now supports the import of predictive models in Predictive Model Markup Language (PMML) version 4.4. With this feature, you can import PMML models that use the anomaly detection algorithm.

For a list of all supported PMML models, see Supported models for import

 

Automate business process tracking by importing Excel files

Valid from Pega Version 8.5

To track business processes status and data, you can now import Excel files when you create a case or data object in App Studio. This functionality provides the following enhancements:

  • You can now upload a CSV file when you create a case or data object in App Studio. By importing a CSV file, you can use the data in your spreadsheet to define your data model.
  • You can generate a data import template that you can use to import a file in its original format during production.
  • You can upload .xlsx files to avoid resaving your Excel file as a CSV file.

For more information, see Creating a data model from a spreadsheet.

Data APIs support data exploration in React UI tables

Valid from Pega Version 8.5

Data APIs have been enhanced to support filtering, sorting, paging, and aggregation in React UI tables. You can use that functionality to access your data quickly and intuitively. For example, by using paging, you can query a data page to retrieve the second page of an employee contact list and specify the number of results that are displayed on the page.

For more information, see Data API performance and limitations.

Support for application-specific REST API calls

Valid from Pega Version 8.5

You can now call an authenticated REST API in the context of any application that is listed on an operator record by using the application alias URL. With the application alias URL, you can also develop REST services without changing the access group in the service package. REST services run in the context of the access group that points to the provided application, instead of the access group that is specified in the service package.

For more information, see Invoking a REST service rule.

Limits on active data flow runs

Valid from Pega Version 8.5

You can now configure a maximum number of concurrent active data flow runs for a node type. Set limits to ensure that you do not run out of system resources and that you have a reasonable processing throughput. If a limit is reached, the system queues subsequent runs and waits for active runs to stop or finish before queued runs can be initiated, starting with the oldest.

For more information see, Limit the number of active runs in data flow services (8.5).

Upgrade impact

If you have many data flow runs active at the same time, you might notice that some of the runs are queued and waiting to be executed.

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

You do not have to take any action. After the active runs stop or finish, the queued runs start automatically. The default limits are intended to protect your system resources, and you should not see a negative impact on the processing of data flows. However, if you want to allow a greater number of active data flow runs to be active at the same time, you can change the limits. For more information, see Limiting active data flow runs.

Failed Robotic Assignments work queue type changed to Standard

Valid from Pega Version 8.5

The default Failed Robotic Assignments work queue type is now Standard. In previous releases, the default type was Robotic. For usage information, see Configuring a work queue for robotic automation.

Upgrade impact

After upgrading to Pega Platform 8.5 and later, you cannot save case types in which you configure the Queue for robot smart shape to route new assignments to the Failed Robotic Assignments work queue. Existing assignments that you routed to the Failed Robotic Assignments work queue are not affected.

How do I update my application to be compatible with this change?

As a best practice, do not use the Failed Robotic Assignments work queue in your custom implementations. Instead, configure the Queue for robot smart shape to route new assignments to a Robotic work queue. When possible, update existing case types to use the robotic work queues that you created in your application.

Support for Apache HBase 2.1 and Hadoop 3.0

Valid from Pega Version 8.5

Support for these versions extends Pega Platform™ compatibility with HBase releases to ensure that your database implementations integrate seamlessly with Pega Platform.

Pega Platform now supports:

  • Apache HBase 2.1 for the HBase data set
  • Apache Hadoop Distributed File System (HDFS) 3.0 for the HDFS data set

For more information, see Enhance your data sets with Apache HBase 2.1 and Hadoop 3.0 (8.5).

Savable data pages support loading pages individually from a page list

Valid from Pega Version 8.5

You can now load individual pages in a page list from single object data pages to your case and data types. This functionality allows you to save autopopulated properties with the Load each page individually option using a flow action, save data page smart shape, or the activity method.

For more information, see Saving data in a data page as part of a flow.

Enhancing your revision management process with Deployment Manager pipelines

Valid from Pega Version 8.5

Pega Platform 8.5 offers improved synergy between revision management and the automated deployment process provided by Pega's Deployment Manager 4.8 pipelines. Use Deployment Manager 4.8 to increase the efficiency of business-as-usual application changes and automatize the deployment of revision packages.

For more information, see Managing the business-as-usual changes.

Support for Cloud AutoML topic detection models

Valid from Pega Version 8.5

In Prediction Studio, you can now connect to topic detection models that you create in Cloud AutoML, Google's cloud-based machine learning service. You can then use the models to categorize and route messages from your customers.

For more information, see Broaden your selection of topic detection models by connecting to third-party services (8.5).

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