Kafka Rulebook for Pega – The Complete Guide

Executive Summary

This guide shows you how to implement an asynchronous, fault-tolerant, event-driven architecture in Pega using Apache Kafka. The goal: decouple inbound API traffic from case creation, ensure no events are lost, support retries through DLQ, and enable outbound Kafka publishing directly from Pega.

Whether you’re working in public transport, banking, or logistics — this pattern gives you resilience, scale, and visibility in one clean package.

Real-World Use Case

Imagine you’re building a public-facing flight booking portal. Thousands of booking submissions flow into your backend hourly.

Traditional approach? REST + Queue Processor = blocked threads, poor visibility, failed retries.

Kafka approach?

• Publish every booking event to Kafka instantly

• Let Pega consume them via Data Flows

• Push failed cases into a DLQ for retry with full audit trail

This isn’t a trend — it’s a battle-tested pattern.

Architecture Overview

Client App → Kafka Topic (booking-events)

↓

Kafka Data Set (consume mode)

↓

Real-Time Data Flow → Case Creation

↓

DLQ Topic (on failure)

↓

DLQ Data Flow + Retry Scheduler

Step-by-Step Kafka Integration in Pega

Step 1: Create Kafka Instance

• Location: Records > SysAdmin > Kafka

• Name: Kafka_Booking_Cloud_Mock

• Host/Port: Use mock values like kafka.booking.mock, 9092

• Authentication: Disabled

• Note: Connection test may fail without Kafka, expected during design

Step 2: Create Data Class

• Class Name: Data-Booking-Incoming

• Key Properties:

  • .bookingId (Text)

  • .passengerName (Text)

  • .flightNumber (Text)

  • .travelDate (Date)

Step 3: Kafka Data Set

• Type: Kafka

• Class: Data-Booking-Incoming

• Kafka Instance: Use instance from Step 1

• Topic Name: booking-events

• Partition Key: .bookingId

• Format: JSON

Step 4: Real-Time Data Flow

• Name: DF-Booking-RealTime

• Applies To: Data-Booking-Incoming

• Nodes:

  • Source: Kafka Data Set

  • Optional Transform: Map to Work class properties

  • Destination: Booking-Work-Booking

• Effect: Automatically listens and creates case per Kafka message

Step 5: DLQ + Retry Strategy

• DLQ Topic: booking-events-dlq

• DLQ Kafka Data Set:

  • Name: DS-Kafka-BookingEvents-DLQ

  • Topic: booking-events-dlq

• Retry Data Flow:

  • Name: DF-Booking-DLQ-Retry

  • Source: DLQ Data Set

  • Destination: Booking-Work-Booking

• Job Scheduler:

  • Name: Retry-Booking-DLQ

  • Frequency: Every 5–10 minutes

  • Task: Triggers retry Data Flow

• Tip: Filter out bad schema or poison messages before retrying

Step 6: Publish Events to Kafka from Pega

• Activity Name: Kafka-PublishMessage

• Class: Booking-Work-Booking

• Java Step:

try

{

KafkaClient.publish(tools, "Kafka_Booking_Cloud_Mock", "booking-events", .bookingId, jsonPayload);

}

catch (KafkaClientException e)

{

oLog.error("Kafka publish failed for key: " + .bookingId, e);

tools.getPrimaryPage().addMessage("Kafka publish failed: " + e.getMessage());

}

• Where to use: Queue processors, utility shapes, async notifications

Logging and Auditing

• Failure Log Sample:

2025-07-01 14:06:21,789 [DLQ-Retry] ERROR - Kafka retry failed for key BKG-27381 - Booking ID not found in payload.

• Recommendation:

  • Add custom audit property. retryCount

  • Write retry logs with original payload for traceability

Security Best Practices

• Use Dynamic System Settings for Kafka instance names and topics

• For production, enable SASL_SSL authentication

• Use separate DLQ topics per feature/module

• Encrypt DLQ payloads if containing PII

Final Checklist

• Kafka instance created

• Data class with event structure

• Kafka Data Set (consume mode)

• Real-Time Data Flow wired to create cases

• DLQ Data Set + Retry Flow

• Job Scheduler for retries

• Kafka Publish Activity (Java)

• Logging and Auditing ready

Closing Thoughts

This Kafka pattern lifts your Pega solution into true modern architecture:

• Async, fail-tolerant, and scalable

• Real-time retries with audit trail

• Replays and observability

It’s not just about using Kafka , it’s about using it right, inside Pega.

#Pega #Kafka #AsyncProcessing #ExecutionEdge004 #DeadLetterQueue #ScalableIntegration