Designing enterprise data access with Pega Live Data

A Lead System Architect (LSA) must understand and apply the core principles and benefits of Pega Live Data in an optimal and efficient way.

Data abstraction and virtualization

Data Pages decouple the data source from the application logic. A user interface or Case can reference a D_CustomerDetails Data Page without needing to determine whether the data originates from a database, a REST service, or robotic automation. This abstraction supports the ability to replace or update data sources with minimal impact on the application.

Declarative on-demand loading

Pega Live Data uses a declarative approach. Data Pages load automatically when referenced and load only when the data is not already in memory or when the Data Page is marked as stale.

Caching with configurable refresh strategies

Data Pages reside in memory and cache their results so that subsequent reuse is immediate and does not repeatedly call external systems. This approach helps you meet performance Service‑Level Agreements (SLAs).

Conditional sourcing

A single Data Page can incorporate logic that selects a source or merges data from multiple sources. For example, a Data Page can load customer data from System A for domestic customers and System B for international customers.

Aggregation

Data Pages support composite data in scenarios where the application retrieves data from more than one system. 

Savable Data Pages

Savable Data Pages provide a unified mechanism for retrieving and updating data through the Data Page abstraction. Updates follow the same integration rules that apply to reads.

The following figure shows data abstraction and virtualization using Pega Live Data:

An LSA must monitor and enforce strict governance for the implementation of Data Pages to ensure data integrity, performance, maintainability, scalability, and security. Adopt the following key practices:

• Minimize performance impact by using parameters effectively. A new Data Page instance loads for each unique parameter combination, so evaluate the cardinality of parameters carefully.
• Clear stale instances when immediate updates are required, especially in production environments where cache consistency is critical.
• Select an appropriate scope for each Data Page based on data‑sharing requirements.
• Align the refresh strategy with the volatility of the data and the Data Page scope. For frequently changing or user‑specific data, such as a credit score, use thread scope with conditional refresh. For static reference data, such as a list of countries, use node scope with an infrequent reload.
• Adopt Savable Data Pages to persist data to a system of record. Savable Data Pages improve performance and reduce memory use. When used with a Flow Action, they also maintain transactionality with the work item.
• Verify that the UI is bound to the Savable Data Page. Data does not persist unless the UI is bound to the correct source. Binding fields to a clipboard page or a read‑only structure bypasses the save plan.
• Apply Access Control Policies to classes when retrieving data from Data Pages to meet security requirements.
• When sourcing data using REST services, role‑based and attribute‑based access controls are not fully applicable. Add a privilege to the Data Page, views, and roles to achieve the required level of security. For more information about available privilege configurations, see Set Data Page security.

Using GenAI Connect Rules as a data source for Data Pages enables you to use generative AI to process unstructured data, convert it to structured data, and use the results in your workflows. Common use cases include:

• Convert unstructured data into structured data.
• Intelligently interpret and transform data.
• Process data that requires intelligent analysis before using the information in Cases.
• Generate realistic test or sample data during development using generative AI.

When you use a GenAI Connect Rule as a data source, configure the inputs first by using an Activity or Data Transform if the information is not available in the current context. Use that input in the GenAI Connect Rule. Then use a Data Transform to map the structured output back to your Case Type or Data Type.

The following image shows how unstructured information from attached documents loads first and is then analyzed by GenAI Connect to produce structured information:

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