Apache Pulsar and Apache Kafka are two of the most popular distributed messaging platforms, each with unique architectural strengths. While Kafka dominates traditional streaming ecosystems, Pulsar offers a modern cloud-native alternative with features like multi-tenancy, native geo-replication, and tiered storage.

In some cases, enterprises may need to integrate Kafka and Pulsar — for reasons like gradual migration, multi-platform interoperability, or connecting isolated domains. This post explores how to build hybrid Kafka-Pulsar architectures, covering bridging tools, design patterns, and real-world integration examples.

Why Integrate Kafka and Pulsar?

Here are common scenarios that demand Kafka-Pulsar integration:

  • ✅ Legacy modernization: Gradually migrate from Kafka to Pulsar
  • ✅ Domain isolation: Use Pulsar for multi-tenant workloads while Kafka handles legacy pipelines
  • ✅ Cross-team collaboration: Bridge Kafka events into Pulsar for independent processing
  • ✅ Resilience and flexibility: Build vendor-agnostic systems with better failover and routing

Architectural Overview

A typical hybrid setup looks like:

[Producers] → [Kafka Topic] → [Kafka Source Connector] → [Pulsar Topic] → [Consumers]

[Pulsar Producers] → [Pulsar Topic] → [Kafka Sink Connector] → [Kafka Topic] → [Consumers]

Kafka and Pulsar can be bi-directionally connected using bridge connectors, allowing seamless message flow between ecosystems.

Integration Tools

1. Pulsar Kafka Compatibility Wrapper

Pulsar includes a Kafka-on-Pulsar (KoP) protocol handler:

  • Allows Kafka clients to connect directly to Pulsar brokers
  • No code changes required on the Kafka producer/consumer side
  • Useful for drop-in migration or co-existence

Enable KoP in Pulsar configuration:

messagingProtocols=kafka
protocolHandlerDirectory=./protocols

Then use standard Kafka libraries to connect to Pulsar as if it’s Kafka.

2. Kafka Connect → Pulsar Sink

Bridge Kafka topics into Pulsar using a Kafka Connect sink:

{
"name": "kafka-to-pulsar",
"config": {
"connector.class": "org.apache.pulsar.io.kafka.connect.KafkaConnectSink",
"topics": "orders",
"pulsar.service.url": "pulsar://broker:6650",
"pulsar.topic": "persistent://public/default/orders",
"tasks.max": "1"
}
}
3. Pulsar IO Kafka Source

Use Pulsar IO connectors to read from Kafka into Pulsar natively:

pulsar-admin sources create \
--source-type kafka \
--source-config-file kafka-source-config.yaml \
--tenant public \
--namespace default \
--name kafka-source

Sample kafka-source-config.yaml:

bootstrapServers: "kafka-broker:9092"
topics: "events"
groupId: "pulsar-bridge"

Real-World Use Case Examples

✅ Use Case 1: Kafka Ingestion → Pulsar Processing
  • Legacy systems push data to Kafka
  • Data is ingested into Pulsar via Pulsar IO Kafka Source
  • Pulsar Streams or Functions handle real-time transformations
✅ Use Case 2: Pulsar Stream to Kafka Analytics
  • IoT devices produce events to Pulsar
  • Events forwarded to Kafka via Kafka Sink for further warehousing
  • Downstream tools like Kafka Connect, Flink consume from Kafka
✅ Use Case 3: Gradual Migration from Kafka to Pulsar
  • Enable KoP in Pulsar
  • Redirect Kafka clients to Pulsar without rewriting app logic
  • Decommission legacy brokers progressively

Schema Compatibility & Interoperability

  • Use Confluent Schema Registry with Kafka
  • Use Pulsar’s built-in schema registry or connect to Confluent
  • For cross-platform schemas, serialize with Avro or Protobuf
  • Ensure consistent field evolution strategies on both ends

Monitoring and Observability

Track hybrid pipelines with:

  • Kafka Exporter for Prometheus metrics
  • Pulsar Exporter or Grafana dashboards
  • Log events between bridge connectors for latency and error tracking

Watch for:

  • Connector lag
  • Replication failures
  • Throughput drops across bridged clusters

Security Considerations

  • Use TLS and mTLS for inter-cluster communication
  • Secure both Kafka and Pulsar with ACLs and authentication providers
  • Avoid topic name collisions when bridging (use namespaces wisely)
  • Audit all access points using broker logs and access control events

Best Practices

✅ Start with uni-directional bridges before going bi-directional
✅ Apply message deduplication where exactly-once is critical
✅ Use schema validation at both source and sink
✅ Monitor connector health and lag constantly
✅ Keep topic naming consistent across systems
✅ Prefer KoP for greenfield Pulsar migrations
✅ Run load tests on hybrid connectors before production

Conclusion

Bridging Apache Pulsar and Apache Kafka empowers organizations to combine the best of both messaging platforms — scalability, flexibility, and ecosystem maturity. Whether you’re modernizing legacy systems or building a polyglot event mesh, Pulsar-Kafka hybrid architectures provide the resilience and extensibility to power real-time, enterprise-grade data pipelines.

By using the right connectors, tuning schema strategies, and following integration best practices, you can create a unified streaming fabric across platforms.