Camunda 8 Kafka Integration – How It Works

 Integrating Camunda 8 with Apache Kafka enables powerful event-driven workflow orchestration.

Camunda 8 manages long-running business processes, while Kafka handles high-throughput event streaming between distributed services. Together, they form a scalable and decoupled microservices architecture.

In this guide, we will cover:

• Why Kafka integration is important

• Integration patterns

• Implementation approaches

• Error handling strategies

• Production best practices

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1️⃣ Why Integrate Camunda 8 with Kafka?

Camunda 8 is designed as an asynchronous, distributed workflow engine using Zeebe. Kafka naturally complements this architecture.

Benefits:

✅ Decoupled services
✅ High scalability
✅ Event-driven orchestration
✅ Resilience and durability
✅ Replay capability

Typical use cases:

• Order created event triggers workflow

• Payment completed event updates process

• Inventory updated event continues fulfillment

• Microservices publish business events

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2️⃣ Camunda 8 Execution Model (Quick Recap)

Camunda 8 works using:

• Zeebe Broker (workflow state)

• Gateway (client entry point)

• Job Workers (external services)

• Message correlation

• JSON-based variables

Unlike Camunda 7, there is no embedded engine inside your application. Workers pull jobs asynchronously.

This design makes Kafka integration very natural.

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3️⃣ Integration Patterns

There are three main patterns.

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🔹 Pattern 1: Kafka → Camunda (Event Triggers Workflow)

Use case:
An external system publishes an event. That event should start or continue a workflow.

Flow:

Kafka Topic → Consumer Service → Camunda Message Correlation

Implementation:

1. Kafka consumer listens to topic

2. When event received:

   • Call Camunda API

   • Start process OR correlate message

Example (Java Kafka Consumer → Camunda):

@KafkaListener(topics = "order-created")
public void consume(String message) {

    zeebeClient.newPublishMessageCommand()
        .messageName("OrderCreated")
        .correlationKey(extractOrderId(message))
        .variables(message)
        .send()
        .join();
}

This is the most common pattern.

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🔹 Pattern 2: Camunda → Kafka (Process Emits Event)

Use case:
When workflow reaches a certain step, it must notify other services.

Flow:

Camunda Service Task → Job Worker → Publish Kafka Event

Implementation:

1. BPMN Service Task (type: publish-event)

2. Worker receives job

3. Worker publishes event to Kafka

Example:

@JobWorker(type = "publish-event")
public void publish(ActivatedJob job) {

    kafkaTemplate.send("payment-completed", job.getVariables());

    jobClient.newCompleteCommand(job.getKey())
        .send()
        .join();
}

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🔹 Pattern 3: Bidirectional Event Orchestration

This is common in microservices systems:

• Camunda publishes event

• Another service processes

• Service publishes result event

• Camunda continues process

Example:
Payment flow:

1. Process sends payment request event

2. Payment service processes

3. Publishes PaymentCompleted event

4. Camunda correlates message

5. Process continues

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4️⃣ Message Correlation Strategy

When integrating with Kafka, always use:

• Stable correlation keys (orderId, customerId)

• Message events in BPMN

• Intermediate message catch events

Example BPMN design:

Start Event
   ↓
Service Task (Publish Event)
   ↓
Intermediate Catch Message (Wait for Kafka Response)
   ↓
Continue

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5️⃣ Error Handling Strategy

Kafka + Camunda must handle failures carefully.

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Case 1: Kafka publish fails

Solution:

• Retry inside worker

• Use backoff strategy

• Throw error only after retry exhaustion

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Case 2: Consumer fails after event read

Solution:

• Use Kafka offset commit properly

• Ensure idempotency

• Avoid duplicate process start

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Case 3: Duplicate events

Kafka guarantees at-least-once delivery.

Solution:

• Make process idempotent

• Use business key uniqueness

• Store processed event IDs

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6️⃣ Exactly-Once Processing (Important Concept)

Kafka is not exactly-once by default.

Camunda does not manage Kafka transactions.

Best practice:

• Use Outbox Pattern

• Ensure idempotent job workers

• Store processed message reference

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7️⃣ Architecture Diagram (Conceptual)

          ┌──────────────┐
          │   Kafka      │
          └──────┬───────┘
                 │
         ┌───────▼────────┐
         │ Consumer Service│
         └───────┬────────┘
                 │
         ┌───────▼────────┐
         │ Camunda 8      │
         │ Zeebe Engine   │
         └───────┬────────┘
                 │
         ┌───────▼────────┐
         │ Job Worker     │
         └───────┬────────┘
                 │
              Kafka

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8️⃣ Production Best Practices

✔ Use idempotent workers
✔ Monitor Kafka lag
✔ Monitor Zeebe backpressure
✔ Separate orchestration from event streaming logic
✔ Keep payloads small (avoid huge JSON blobs)
✔ Use retry + dead letter topics

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9️⃣ When NOT to Use Kafka

• Simple synchronous APIs

• Small monolithic systems

• Low throughput environments

Kafka adds complexity. Use only when needed.

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🔟 Final Thoughts

Camunda 8 and Kafka together create a powerful event-driven orchestration system.

Kafka handles event streaming.
Camunda manages business logic and state.

When designed correctly, this architecture becomes:

• Highly scalable

• Fault tolerant

• Cloud-ready

• Enterprise-grade

💼 Professional Support Available

If you are facing issues in real projects related to enterprise backend development or workflow automation, I provide paid consulting, production debugging, project support, and focused trainings.

Technologies covered include Java, Spring Boot, PL/SQL, Azure, and workflow automation (jBPM, Camunda BPM, RHPAM).

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