Apache Kafka vs Apache Pulsar – Which one to choose?

In today’s data-driven world, the ability to process and analyze real-time data streams is crucial for businesses. Two open-source platforms, Apache Kafka and Apache Pulsar, have emerged as leaders in this space. But which one is right for you?

Market Share and Community:

• Apache Kafka: Commands a dominant 70% market share, boasting a vast user base and extensive ecosystem of tools and libraries. This makes it more appropriate for organizations to adopt it.
• Apache Pulsar: Though holding a smaller 30% share, Pulsar is rapidly gaining traction, especially among cloud-native companies and those valuing its unique features.

Pros and Cons:

Apache Kafka:

Pros:

• Mature and proven: With years of development and refinement, Apache Kafka offers stability and reliability.
• Extensive ecosystem: A vast collection of connectors, libraries, and tools ensures seamless integration with various technologies.
• High performance: Kafka scales effortlessly to handle massive data streams, making it ideal for demanding workloads.
• Stream processing powerhouse: Kafka’s built-in stream processing capabilities simplify real-time data analytics.

Cons:

• Complexity: Managing Kafka, with its ZooKeeper dependency, can be challenging for smaller teams. But, with KIP-833 we can run Kafka cluster without Zookeeper.
• Resource-intensive: Running Kafka can be resource-intensive, requiring high-performance infrastructure.
• Limited multi-tenancy: Kafka primarily focuses on single-tenant deployments, limiting its use in some scenarios.
• Lacking of native support for Multi-DC cluster setup with geo replication. Though there is Kafka Mirrormaker and the stretched cluster concept, it has some performance impact.

Apache Pulsar:

Pros:

• Cloud-native design: Apache Pulsar was built for the cloud, offering seamless integration with cloud platforms and micro-services architectures.
• Multi-tenancy built-in: Pulsar allows for secure and efficient sharing of resources across multiple users and applications.
• High scalability: Pulsar’s tiered storage architecture enables horizontal scaling to handle enormous data volumes.
• Low latency: Pulsar excels at low-latency data processing, making it ideal for time-sensitive applications.
• Native Multi-DC cluster setup with geo replication.

Cons:

• Maturity gap: Compared to Kafka, Pulsar’s ecosystem is still under development, with fewer readily available tools and libraries.
• Smaller community: While growing, Pulsar’s community is smaller than Kafka’s, potentially leading to limited support resources.
• Stream processing capabilities: Though improving, Pulsar’s stream processing capabilities are not as mature as Kafka’s.

Use Cases:

Common to both:

• Real-time analytics: Analyze data streams in real-time for immediate insights and decision-making.
• Log aggregation: Collect and analyze log data from various sources for centralized monitoring and troubleshooting.
• Microservices communication: Connect and communicate between microservices in a distributed system.
• IoT data processing: Process and manage data streams generated by IoT devices for real-time monitoring and control.

Specific to Kafka:

• Building message-driven applications: Leverage Kafka’s messaging capabilities to build highly scalable and distributed applications.
• High-throughput data pipelines: Kafka excels at handling large volumes of data with minimal latency, making it ideal for data pipelines.

Specific to Pulsar:

• Cloud-based deployments: Pulsar’s cloud-native design makes it perfect for deploying real-time data streaming applications in the cloud.
• Multi-tenant environments: Pulsar’s multi-tenancy capabilities allow for secure and resource-efficient sharing of data pipelines across multiple organizations.

Production Deployers:

Apache Kafka:

• Netflix: Processes billions of events daily for personalization, recommendations, and real-time analytics.
• LinkedIn: Handles millions of messages per second for feed updates, notifications, and social graph management.
• Uber: Uses Kafka to power its real-time tracking and matching systems for drivers and passengers.
• More details https://kafka.apache.org/powered-by

Apache Pulsar:

• Yahoo: Leverages Pulsar for its real-time advertising platform, managing billions of events per day.
• Tencent: Utilizes Pulsar to handle trillions of messages daily for its messaging services and social media platforms.
• BMW: Uses Pulsar for its connected car platform, processing real-time data from millions of vehicles.
• More details https://pulsar.apache.org/case-studies/

Trends:

• Hybrid deployments: Organizations are increasingly combining Kafka and Pulsar to benefit from each platform’s strengths.
• Serverless integration: Both platforms are integrating with serverless functions for a more flexible and cost-effective approach.
• Edge computing: Both Kafka and Pulsar are finding application in edge computing scenarios for decentralized data processing.
• Multi-tenancy is key: Platforms with strong multi-tenancy features are becoming increasingly important, particularly in cloud-based environments.

Choosing between Apache Kafka and Apache Pulsar requires careful consideration of your specific needs and priorities. Try to do benchmarking for your use cases, test for availability and scalability before choosing any one for implementation. There are some benchmarking results available for initial glance from https://www.confluent.io/kafka-vs-pulsar/ . We will meet in next blog post, until then, Happy Messaging!!!