As real-time data processing becomes critical for applications ranging from fraud detection to personalization, the choice of a robust event streaming platform is more important than ever. Apache Kafka and Apache Pulsar are two leading open-source platforms that power event-driven architectures.

This blog presents a detailed comparison of Kafka vs. Pulsar, examining their architecture, performance, scalability, and operational features to help you choose the right tool for your event streaming needs.

Overview of Apache Kafka

Apache Kafka is a distributed publish-subscribe messaging system originally developed at LinkedIn and later open-sourced via the Apache Foundation.

Key characteristics:

  • Distributed, high-throughput, durable messaging
  • Append-only commit log with partition-based storage
  • Strong ecosystem (Kafka Streams, ksqlDB, Kafka Connect)
  • Wide community and cloud-native support

Kafka is ideal for high-throughput, persistent event streaming pipelines.

Overview of Apache Pulsar

Apache Pulsar, developed at Yahoo and now part of the Apache Software Foundation, is a next-generation pub-sub messaging system.

Key characteristics:

  • Built-in multi-tenancy and geo-replication
  • Segmented storage architecture with Apache BookKeeper
  • Serverless function support
  • Native support for both streaming and queue-based messaging

Pulsar’s decoupled architecture allows flexible scaling and lower storage latency.

Architectural Comparison

Feature Apache Kafka Apache Pulsar
Storage Layer Broker-based (monolithic) Segmented (Brokers + BookKeeper)
Multi-Tenancy Not native (requires workarounds) Built-in with namespace isolation
Message Queuing Streams only Streams + Queues (flexible model)
Geo-Replication MirrorMaker / Confluent Replicator Built-in (via Brokers)
Topic Scalability One topic = one log = one file Topic segments = independent scaling
Consumer Models Consumer groups, pull-based Exclusive, shared, failover (push/pull)
Durability High (replication-based) High (BookKeeper ledger replication)
Latency Low (with tuning) Lower latency out-of-the-box

Performance and Scalability

  • Kafka achieves high throughput by batching writes and reads, but performance tuning is required for large numbers of partitions.
  • Pulsar decouples storage from compute using BookKeeper, allowing better horizontal scalability.

Kafka scales via partitions per topic, while Pulsar uses topics with segments, which makes scaling more granular and independent of broker memory.

Message Processing Models

  • Kafka supports pull-based consumption only.
  • Pulsar supports pull and push-based consumption and multiple subscription modes:
    • Exclusive
    • Shared (round-robin)
    • Failover

This makes Pulsar more flexible for use cases like real-time alerting, ML model inference pipelines, or guaranteed ordering per consumer.

Operational Differences

Category Kafka Pulsar
Deployment Simple but requires Zookeeper More complex: Brokers + BookKeeper + ZooKeeper
Tooling Kafka Connect, ksqlDB, Confluent Control Center Pulsar Manager, Functions, Pulsar IO
Cloud-Native Kafka on Confluent Cloud, MSK, Aiven Pulsar on StreamNative, AWS EKS
Upgrade Path Manual topic migration in some versions Seamless upgrade support for tenants
Monitoring JMX, Prometheus exporters Built-in Prometheus integration

Ecosystem and Integration

  • Kafka’s ecosystem is more mature, especially with Kafka Streams, ksqlDB, and Kafka Connect.
  • Pulsar is catching up with:
    • Pulsar Functions (lightweight compute)
    • Pulsar IO for ETL connectors
    • Native Flink, Spark, and NiFi integrations

For greenfield projects, Pulsar offers flexibility. For legacy compatibility or proven stability, Kafka has the upper hand.

When to Choose Kafka

Choose Apache Kafka when:

  • You need high throughput and simple stream processing
  • Your use case benefits from a mature ecosystem (Kafka Streams, Confluent)
  • You want community support and available expertise
  • You’re already using Kafka in your architecture

When to Choose Pulsar

Choose Apache Pulsar when:

  • You require multi-tenancy or tenant isolation
  • You’re building a hybrid streaming and queuing system
  • You want out-of-the-box geo-replication
  • You need better horizontal scalability at the storage level
  • You want serverless functions natively in your messaging stack

Conclusion

Both Apache Kafka and Apache Pulsar are powerful tools for event streaming. Kafka excels in simplicity and ecosystem maturity, while Pulsar shines in flexibility, scalability, and modern architecture.

Ultimately, the right choice depends on your project requirements, team expertise, and long-term goals for your data platform. Use Kafka for proven pipelines and Pulsar when you need a cloud-native, multi-tenant, function-ready messaging platform that scales with your applications.