ActiveMQ vs Kafka: Differences & Use Cases Explained

What is Apache Kafka?

Apache Kafka was originally developed by LinkedIn to handle the company’s real-time data feeds and was open-sourced in early 2011. 

In 2012, Kafka was contributed to the Apache Software Foundation (ASF), where it became a top-level project. Since then, it has been maintained and developed by the open-source community under the governance of the Apache Software Foundation, with significant contributions from organizations like LinkedIn, Confluent, and others.

Kafka is a distributed event streaming platform designed for high-throughput, fault-tolerant, and scalable messaging. It is written in Scala and Java, and its architecture is optimized for handling real-time data streams, making it suitable for building data pipelines and event-driven applications. 

Kafka is well-regarded for its ability to process large volumes of data with low latency, robust data retention, and replayability. Its extensive ecosystem includes Kafka Connect and Kafka Streams for integrating with other data systems and processing streams in real time.

Features of Kafka

Here are the most prominent features of Kafka:

• High throughput and low latency: Optimized for high-throughput and low-latency processing, making it suitable for real-time applications.
• Scalable and distributed: It can scale horizontally by adding more brokers to the cluster, allowing it to handle large volumes of data.
• Fault tolerance and durability: Ensures data durability with replication across multiple nodes and provides built-in fault tolerance.
• Event streaming: Supports event streaming and real-time data processing, which is helpful for log aggregation, stream processing, and data integration.
• Replayability: Retains data for a configurable period, allowing consumers to replay and reprocess events as needed.
• Ecosystem: Offers a robust ecosystem, including Kafka Connect, Kafka Streams, and integration with numerous big data and analytics tools.

Use cases for Kafka

These are the most relevant and popular use cases for Apache Kafka:

• High-throughput streaming: Ideal for applications that require processing large volumes of data in real-time, such as log aggregation and event sourcing.
• Scalable microservices architectures: Suitable for microservices that need scalable, fault-tolerant, and distributed messaging.
• Data pipeline and analytics: Commonly used for building real-time data pipelines and analytics platforms where data durability and replayability are crucial.

If you’re considering using Kafka for your real-time data or are simply curious about this technology, take the Introduction to Apache Kafka course to get up to speed!

ActiveMQ vs Kafka: Main Differences

Choosing the right messaging platform for your application is important to guarantee efficient data flow, scalability, and system reliability.

As we saw before, while both ActiveMQ and Apache Kafka serve as message brokers, they cater to different needs and use cases. Each offers a unique architecture, performance characteristics, and operational features.

Let’s review the main differences of these message brokers in different categories:

Architecture

ActiveMQ uses a broker-centric architecture, storing messages in a central broker that manages queues and topics. This setup relies on message acknowledgments and persistent storage to ensure reliable delivery and durability of messages. 

In contrast, Kafka utilizes a distributed log architecture, where data is partitioned and replicated across multiple brokers in a cluster. Kafka’s design decouples producers and consumers, which allows for high scalability and performance, making it ideal for handling large volumes of data in distributed systems.

Messaging model

ActiveMQ supports both point-to-point (queues) and publish-subscribe (topics) models, providing flexibility for a range of messaging scenarios with a strong focus on message delivery guarantees. 

On the other hand, Kafka primarily operates using a publish-subscribe model built on distributed logs. This approach allows multiple consumers to read data independently and at their own pace from the same partition, which is advantageous for applications that require high throughput and concurrent processing.

Performance and throughput

When it comes to performance and throughput, ActiveMQ is suitable for use cases that require moderate throughput and lower latency, but its performance can be affected by the need for persistent messaging and complex routing through a central broker. This can limit its ability to handle extremely high data volumes efficiently. 

Kafka, in comparison, is designed for high throughput and low latency, capable of processing millions of messages per second. Its architecture is optimized for real-time data streaming, making it a preferred choice for applications that demand continuous, high-speed data processing, such as log aggregation and real-time analytics.

Scalability

ActiveMQ can scale through clustering and a network of brokers, but this process can be complex and often requires careful configuration and management. 

Kafka, however, is built with scalability in mind, allowing easy horizontal scaling by adding more brokers to the cluster. Kafka’s partitioning and replication strategies facilitate efficient data management across a large number of nodes, enabling it to handle increased loads with minimal configuration effort.

Fault tolerance and durability

ActiveMQ uses persistent storage options and broker clustering with failover mechanisms to provide message durability and ensure the system can recover from failures without data loss.

On the other hand, Kafka provides robust fault tolerance through data replication across multiple brokers in the cluster, ensuring high durability even during broker failures. Kafka’s ability to replay messages from its log storage adds an additional layer of reliability, allowing consumers to reprocess data as needed.

Ecosystem and integration

ActiveMQ is well-suited for enterprise applications, particularly those requiring JMS compliance. It supports a variety of messaging protocols, including AMQP, STOMP, and MQTT, making it a versatile choice for connecting diverse systems and applications.

Kafka, in contrast, has a rapidly growing ecosystem that includes tools like Kafka Connect for integration with various data sources and sinks, and Kafka Streams for real-time data processing. Its integration capabilities extend to big data platforms, cloud services, and other modern technologies, making it a powerful option for building scalable, real-time data pipelines and event-driven architectures.

ActiveMQ vs Kafka: A Summary

The following table summarizes the differences between ActiveMQ and Kafka across the previously discussed categories:

Now, let’s explore the ideal use cases for each one of these brokers. 

When to Use ActiveMQ

ActiveMQ is a robust option for a variety of messaging scenarios, mainly when dealing with legacy systems, enterprise messaging needs, or specific compliance requirements. 

Here’s a more detailed look at its suitability for different use cases:

Legacy systems integration

ActiveMQ is particularly well-suited for integrating with legacy systems that depend on the Java Message Service (JMS) API. It implements the JMS specification, making it an ideal choice for applications that need to communicate with older systems designed around JMS standards.

Additionally, ActiveMQ supports point-to-point messaging (queues) and publish-subscribe messaging (topics), which can be essential for ensuring compatibility with existing infrastructure that relies on these messaging paradigms.

Enterprise message

ActiveMQ offers significant advantages in enterprise environments where advanced security features, compliance, and flexible deployment options are important. 

It provides robust security mechanisms essential for maintaining message confidentiality and integrity. Its compliance with JMS standards ensures it can integrate seamlessly with other enterprise systems that adhere to the same standards.

Furthermore, ActiveMQ supports various deployment options, such as standalone brokers, clustered configurations, and networked brokers, allowing enterprises to choose the deployment model that best fits their infrastructure and operational needs.

Lower throughput requirements

ActiveMQ is a strong candidate for applications that do not require extremely high throughput but need reliable and low-latency messaging. Its design accommodates scenarios where moderate messaging volumes are expected, and its focus on message delivery guarantees ensures that messages are reliably transmitted even in complex environments. 

The persistence and acknowledgment mechanisms of ActiveMQ provide the reliability needed for applications where message integrity is critical but where the volume of messages is manageable within its throughput capabilities.

ActiveMQ Classic main components. Image source: ActiveMQ documentation.

When to Use Apache Kafka

Kafka is particularly well-suited for scenarios that require high throughput, exceptional scalability, and advanced data handling capabilities. 

Here’s a detailed look at when to use Kafka:

High-throughput streaming

Kafka is a top choice for applications that require real-time data processing and high throughput. It excels in log aggregation and event-sourcing scenarios, where huge amounts of data must be collected, processed, and analyzed in real time.

Kafka's architecture is optimized for handling millions of messages per second with low latency, making it ideal for streaming applications that demand continuous data flow and minimal delay.

Scalable microservice architectures

Kafka is highly effective in supporting scalable microservices architectures. It provides a distributed, fault-tolerant messaging system that can handle the dynamic and distributed nature of microservices. 

Kafka's ability to decouple producers and consumers allows microservices to communicate efficiently, even as the number of services and data volume grows. Its robust fault tolerance ensures that messages are reliably delivered and processed, even in complex, distributed environments.

Data pipeline and analytics

Kafka stands out when building real-time data pipelines and analytics platforms due to its robust data durability and replayability features. Its distributed log architecture allows for reliable data storage and retrieval, enabling the construction of sophisticated data pipelines that can handle high-volume data ingestion and processing. 

Its capability to retain and replay messages from its logs is critical for analytics platforms that process historical data and support complex analytical queries.

The anatomy of an application that uses the Kafka Streams library. Image source: Kafka documentation.

Conclusion

Choosing between ActiveMQ and Kafka depends on your specific needs and use cases. ActiveMQ is well-suited for traditional messaging scenarios, particularly in enterprise settings requiring JMS compliance and lower throughput. In contrast, Kafka shines in high-throughput, scalable environments, making it ideal for real-time data processing and streaming. 

For further reading and to explore more about data engineering, check out the resources below:

• Understanding Data Engineering course
• Streaming Concepts course
• Introduction to Kafka course
• Apache Kafka for Beginners tutorial