Modern applications increasingly rely on real-time event processing to power features like fraud detection, recommendation engines, and observability dashboards. Among the leading technologies in this space are Apache Pulsar, Apache Kafka, and Apache Flink.

While all three are designed for event-driven data pipelines, they differ significantly in their architecture, processing model, and ecosystem integration.

In this blog post, we’ll compare Apache Pulsar’s event processing capabilities to those of Kafka and Flink, helping you choose the right tool for your specific needs.

Quick Overview

Feature Apache Pulsar Apache Kafka Apache Flink
Primary Role Messaging + Lightweight Processing Messaging + Basic Streaming Full-fledged Stream Processing
Built-in Processing Pulsar Functions, IO Connectors Kafka Streams, ksqlDB Rich processing engine (CEP, ML)
Compute & Storage Decoupled (BookKeeper) Coupled External
Event Time Support Basic Moderate Advanced (Watermarks, Windows)
State Management Lightweight Embedded RocksDB (Streams) Full stateful (RocksDB, HA)
Use Case Fit Lightweight, distributed apps General purpose pipelines Heavy analytics, ETL, CEP

Apache Pulsar for Event Processing

Apache Pulsar is primarily a distributed messaging system, but includes native lightweight compute capabilities via:

  • Pulsar Functions: Small, stateless event processors (like AWS Lambda)
  • Pulsar IO: Source and sink connectors for stream integration
  • Windowing and stateful operators via Pulsar Functions SDK

Pulsar is ideal for:

  • Lightweight event transformation
  • Stateless enrichment
  • Simple aggregations
  • Decoupled microservices

Example Pulsar Function in Python:

def process(event, context):
if event['value'] > 100:
context.publish("alerts", "High value detected: " + str(event))

Apache Kafka for Event Processing

Kafka includes Kafka Streams and ksqlDB for event processing:

  • Kafka Streams: Java library for building distributed stream processing apps
  • ksqlDB: SQL-like interface for streaming queries

Kafka Streams supports:

  • Windowed joins and aggregations
  • Persistent state with changelog topics
  • Exactly-once processing semantics

Kafka is ideal for:

  • Stream joins, transformations, filtering
  • Stateful processing within microservices
  • Integrating with existing JVM-based platforms

Example Kafka Streams pipeline:

builder.stream("orders")
.filter((k, v) -> v.amount > 100)
.to("high-value-orders");

Apache Flink is a stream-first distributed data processing engine that supports:

  • True event-time semantics with watermarks
  • Complex Event Processing (CEP)
  • Fault-tolerant stateful applications
  • Batch and stream unification

Flink is ideal for:

  • Time-based windowing and out-of-order event handling
  • Large-scale aggregations and joins
  • ML pipelines and CEP use cases

Example Flink use case:

  • Detecting sequences of suspicious events
  • Recomputing KPIs in near real time
  • Executing SQL on event streams

Architectural Differences

Component Pulsar Kafka Flink
State Storage BookKeeper (external) Broker disk + RocksDB RocksDB with checkpointing
Processing Layer Functions (built-in) Streams API, ksqlDB Flink Runtime (separate cluster)
Deployment Stateless + lightweight compute Embedded apps or external DBs Requires separate Flink cluster
Scaling Horizontal (compute/storage) Partition-based Dynamic resource management

Integration and Ecosystem

  • Pulsar: Pulsar Functions, Flink/Pulsar connector, Pulsar IO
  • Kafka: Kafka Connect, Kafka Streams, ksqlDB, Confluent Hub
  • Flink: Native Kafka connector, rich SQL/CEP ecosystem, Kubernetes-native deployment

Kafka and Flink offer deeper and more mature stream processing capabilities, but Pulsar’s lightweight model is easier to operate and scale for event-routing and microservice coordination.

When to Use Which?

Use Case Best Fit
Lightweight routing and filtering Pulsar
Stream processing inside Java apps Kafka Streams
Event transformation via SQL ksqlDB
Real-time analytics with late events Flink
IoT ingestion and alerting Pulsar or Kafka
CEP or ML inference on streams Flink
Serverless event functions Pulsar Functions

Conclusion

Apache Pulsar, Kafka, and Flink all serve critical roles in modern event-driven architectures — but their strengths differ significantly.

  • Choose Pulsar for high-throughput messaging, lightweight functions, and multi-tenancy
  • Choose Kafka for stream processing within applications, SQL access, and ecosystem maturity
  • Choose Flink for complex, large-scale, real-time data analytics

In many real-world scenarios, combining these tools provides the best of all worlds — Pulsar for transport, Kafka for middleware logic, and Flink for analytical computation. The right architecture depends on your scale, latency requirements, and operational preferences.