Real-Time Stream Processing: Kafka vs Pulsar vs Flink
- Brinda executivepanda
- Jul 28
- 2 min read
As real-time data becomes essential for business decisions, choosing the right stream processing framework has never been more critical. Apache Kafka, Apache Pulsar, and Apache Flink are three major players in this space—each with strengths suited to different needs. Let’s break down how they compare.
Apache Kafka: The Proven Workhorse Kafka is one of the most mature and widely adop
ted platforms for real-time messaging and stream processing. It offers strong durability, horizontal scalability, and a vibrant ecosystem. Kafka is ideal for event sourcing, log aggregation, and real-time analytics pipelines.
Apache Pulsar: The New Contender Pulsar builds on Kafka’s strengths while addressing some of its limitations. It separates storage from compute, enabling better scalability and lower latency. With built-in multi-tenancy and geo-replication, Pulsar is suited for complex enterprise-level architectures.
Apache Flink: Beyond Messaging Unlike Kafka and Pulsar, Flink is not just a messaging system—it’s a powerful stream processing engine. It supports stateful processing, complex event processing, and windowing. Flink is ideal for real-time ETL, fraud detection, and machine learning pipelines.
Key Comparisons
Latency: Pulsar and Flink often provide lower latency compared to Kafka.
Scalability: Pulsar's architecture offers more flexibility with independent scaling of brokers and storage.
Processing Model: Kafka uses external stream processors like Kafka Streams; Flink is a full-fledged processor.
Complexity: Kafka is easier to set up; Pulsar and Flink require more expertise.
Use Cases: Kafka for log aggregation, Pulsar for distributed systems, Flink for complex real-time processing.