This scenario requires a two-part solution: a bulk export of historical data and a near real-time mechanism for new data.

Option B is ideal because it uses a BIX (Business Intelligence Exchange) extract rule for the historical data. BIX is Pega's purpose-built, high-performance feature for extracting large volumes of data for external reporting and data warehouses. For ongoing cases, triggering a REST post upon resolution is a standard pattern.

Option C is also an ideal, modern solution. It uses a data flow to read historical cases and a Queue Processor to handle the REST API calls asynchronously. This is a scalable and resilient pattern for bulk background processing. Reusing the same Queue Processor for newly resolved cases is efficient and meets the near real-time requirement without impacting user performance.

A: Performing a synchronous REST call within a data flow for a large historical dataset is inefficient, not scalable, and risks timeouts, making it a poor design choice.

D: Using a utility with Obj-Browse to read three years of case data is highly inefficient and can cause severe performance and memory issues. It is not a best practice for large datasets.

1. BIX/Extract Rule (for Option B): Pega Platform™ 8.8 Help

"Creating and running a BIX extract." This document describes using BIX for extracting data from Pega applications for use in external systems

which is the exact requirement for the historical data. It notes BIX is designed for high-throughput data extraction.

2. Data Flows and Queue Processors (for Option C): Pega Platform™ 8.8 Help

"Data flows." This source explains that data flows are the primary mechanism for processing large amounts of data in the background. The section "Data flow processing" details how data flows can be used for batch processing and can output to various destinations

including activities that queue to a Queue Processor.

3. Asynchronous Processing with Queue Processors (for Option C): Pega Platform™ 8.8 Help

"Queue Processor rules." This document explains that Queue Processors provide scalable

asynchronous processing. Using a Queue Processor to call a REST service decouples the main processing thread (either the data flow or the user's case processing) from the integration

improving performance and reliability

which is ideal for both the bulk and near real-time requirements.

4. Inefficiency of Obj-Browse for Bulk Operations (for Option D): Pega Lead System Architect (LSA) courseware and Pega Platform™ 8.8 Help on "Performance guidelines for activities" consistently advise against using Obj-Browse for large datasets due to its impact on memory and database performance. Report definitions or data flows are the recommended alternatives.