Is Kafka a Message Broker?


A message broker is a component of a software system that allows for the interchange of messages. These messages could be application logs or user-created events. Message sending is essential for application monitoring, asynchronous operations, and informing users of necessary actions.

There are many message brokers on the market, from Apache Kafka to Memphis. These brokers share some common features, but mainly enable data transmission between different parts of a system. This article dives into the definition of a message broker and examines whether Apache Kafka is a true message broker.

What is a Message Broker?

A message broker is a software that acts as an intermediary between different applications, routing data between them. It operates on the principles of the `log` data structure, which is similar to a queue. This data structure allows different clients to read messages from the latest or from an offset point in the past.

Basic message broker architecture

Message Broker core features

Message brokers are an essential component of any event-driven or inter-service communication system. They act as a middleman between different parts of a system, routing messages from one end to another in a reliable and secure manner. Some of the core features of message brokers include message routing, queuing, publish-subscribe functionality, and security measures such as message encryption and hashing. This section provides a more in-depth look at the various features of a traditional message broker.

Message Routing

One of the primary functions of a message broker is to route messages between different clients and services. This is done by using a routing key or a set of rules to determine where a message should be sent. This allows for messages to be delivered to the correct recipients without the need for direct connections between the sender and the receiver.

Message Queueing

Another key feature of a traditional message broker is the ability to queue messages. This allows for messages to be stored temporarily if the recipient is not currently available to receive them. The message broker will then deliver the messages when the recipient becomes available. This ensures that messages are not lost and that they are delivered in the correct order.


A traditional message broker also supports transactional messaging. This means that the message broker can ensure that messages are delivered reliably and in the correct order. This is done by using a two-phase commit protocol to ensure that messages are only delivered if they have been successfully committed to the message broker's storage.

External Connections

A traditional message broker also allows for external connections. This means that it can connect to other systems and services that may be running on different platforms or in different locations. This allows for messages to be exchanged between different systems and services in a seamless and efficient manner.

These features of message brokers lend them to be an essential component of large-scale systems. There are different types of such message brokers. IBM MQ is one of the traditional message broker systems that have modernized over the years. Other examples include Active MQ and Rabbit MQ. But then, does Apache Kafka fit well into the traditional message broker definition? Is it more? Find out in the next section.

What is Apache Kafka

So with all the elaborated features and capabilities of traditional message brokers, can we say Kafka checks all these marks? Yes, in fact, Kafka has more features than those outlined. Before going further on what Kafka can do more than a traditional message broker, it’s important to understand what Kafka is.

Apache Kafka is an open-source distributed event streaming platform that follows a publish-subscribe messaging model. This means that message senders publish their messages without any idea of the receivers. The receivers subscribe to the type of messages they need without knowing the sender. Kafka acts as an intermediary between these publishers and subscribers. The publisher’s messages are sent through topics, which are essentially pipelines for messages.

Kafka implements partitions across different message brokers in the distributed system. So one topic can have many partitions and these partitions are stored in different brokers. The master system like Zookeeper can then orchestrate allocation of data to these partitions based on laid-down rules. Take for example a system for sending match updates as they happen. You could design the system with a topic called `match-updates` that has partitions for every team playing. These partitions can then be distributed across the message brokers in the system.

Different parts of Kafka

Kafka has two main components:

  1. The message broker;
  2. The coordinator service that allocates partitions;

Formerly, Zookeeper was officially used for coordination, but this is not the case for more recent Kafka versions. Recent versions now use KRaft.


Building applications with Apache Kafka

Apache Kafka is a powerful, distributed messaging platform that can be used to build a wide variety of applications and systems. One of the key advantages of using Kafka is its ability to handle high volumes of data and support real-time data processing, making it a great choice for big data and streaming applications. This section goes over the different types of applications that can be built using Apache Kafka.


  1. Log Aggregation: One of the most common use cases for Kafka is log aggregation. By collecting log data from multiple sources and sending it to a Kafka cluster, organizations can centralize their logs, making it easier to troubleshoot failures. Kafka is a popular choice for log aggregation because of its high-throughput, scalability, and durability. It achieves high-throughput by using a distributed architecture, where logs are produced to topics, and consumers can subscribe to topics to receive logs in real-time. Additionally, Kafka provides features such as compression and serialization, which can be used to optimize the storage and transmission of log data.


  1. Event-Driven Architecture: Another popular use case for Kafka is building event-driven architectures. In this type of architecture, instead of capturing the state of a system, events are continuously collected and processed in real-time. Kafka can be used to collect and transmit these events, allowing for real-time processing using tools such as Kafka Streams or Apache Spark. This type of architecture is particularly useful for applications that require real-time data processing, such as fraud detection and payment processing.


  1. Data Integration: Kafka can serve as a loading medium in an ELT (Extract Load Transform) pipeline. It could also stream ingested data to the services that perform data transformation. Kafka can work with stream processing applications like Kafka Streams and Apache Spark to perform data streaming within the data system. 


  1. Streaming Applications: Kafka can also be used to build streaming applications, such as video streaming or IoT applications. For video streaming, Kafka can be used to ingest, distribute, and process video data in real-time. For example, you can use Kafka to collect video data from cameras or other sources, process the data to extract metadata, and distribute the processed data to multiple consumers for analysis and reporting. For IoT applications, Kafka can be used to collect and process real-time data streams from IoT devices. For example, you can use Kafka to collect data from sensors, process the data to identify patterns or anomalies, and trigger actions based on specific conditions.


  1. Microservices: Kafka can be used to implement microservices-based architecture. By using a message queue, microservices can communicate with each other in a decoupled way, which makes the system more fault-tolerant and scalable.

How Kafka differs from a traditional message broker

One of the key differences between Kafka and traditional message brokers is the way they handle data. Traditional message brokers, such as RabbitMQ and ActiveMQ, work by storing messages in a queue and routing them to the appropriate consumer. In contrast, Kafka uses a publish-subscribe model, where messages are written to a topic and consumed by one or more subscribers. This allows Kafka to handle much higher volumes of data, making it a great choice for big data and real-time streaming applications.

Another key difference between Kafka and traditional message brokers is the way they handle data replication and fault tolerance. Traditional message brokers often rely on a single point of failure, such as a master-slave configuration, which can lead to data loss or downtime in the event of a failure. In contrast, Kafka uses a distributed architecture, where data is replicated across multiple servers. This ensures that data is always available and protected, even in the event of a failure.

Additionally, traditional message brokers often have a limited set of features and are geared towards small-scale, simple use cases. Kafka, on the other hand, has a wide range of features, including support for data compression, partitioning and data retention. This makes it a versatile choice for a variety of use cases, such as real-time data processing, event streaming, and data integration.

Downsides of Kafka

Kafka, though powerful and widely-used has some downsides that should be taken into consideration when deciding whether it is the right choice for a particular application or system. Some of the downsides of Kafka include:


  1. Complexity: Kafka is a highly configurable and flexible platform, but this can also make it quite complex to set up and manage. The platform requires a good understanding of distributed systems and the ability to configure and tune various parameters to ensure optimal performance.


  1. Scalability: While Kafka is designed to handle high volumes of data and support real-time data processing, it can become a bottleneck when scaling to very large clusters. This is because the platform relies on a shared-nothing architecture, which can lead to contention and poor performance as the number of nodes increases.


  1. Limited Storage: Kafka stores data on disk, which makes it relatively fast when reading and writing data. However, the amount of data that can be stored is limited by the size of the disks. The default retention period for a topic is 7 days and can be increased, but if you need to store data for longer periods, you may need to consider other solutions.


  1. Latency: Kafka is designed to handle high-throughput data streams, but it can introduce some latency when processing data. This is because messages must be written to disk and replicated across multiple nodes before they can be consumed. This can be mitigated by increasing the number of nodes in the cluster and by optimizing the configuration of the system.


  1. Security: Kafka provides some built-in security features, such as authentication and encryption, but it is not as robust as other messaging platforms when it comes to security. To make sure that you are using a robust security system, it is important to implement additional security measures such as network isolation and access control.

Alternative Solutions is a new player in the world of messaging systems, but it has quickly gained popularity due to its superior performance and ease of use compared to other systems like Kafka.

One of the biggest advantages of is its ability to handle high volumes of data with ease. Its unique architecture allows it to handle millions of messages per second, making it a great choice for applications that require real-time data processing. One of the biggest advantages of is its superior developer experience. Memphis handles most of the boilerplate code under the hood so you write less and achieve more. It simplifies the handling of too many data sources and complex schemas, making it easier to transform and analyze streamed data per source. Memphis provides real-time processing without sacrificing performance and efficiency. It also reduces the risk of message loss and offers better observability, making it easier to debug and troubleshoot the event's journey.

Another key advantage of is its user-friendly interface. Setting up and configuring a Kafka cluster can be a complex and time-consuming process, but with, it is easy to get started and manage the system. This makes it a great option for developers who want to focus on building their application, rather than spending time on managing the underlying infrastructure.

In addition to its performance and ease of use, also offers a wide range of features that make it a versatile choice for a variety of use cases. For example, its built-in support for data replication and fault tolerance ensures that your data is always available and protected. Additionally, it supports a schema registry called Schemaverse that can be used for schema retrieval and validation. Schemaverse supports JSON, Avro, GraphQL, and Protobuf. Since Memphis is Kubernetes-native, it has greater cost efficiency, enabling you use 100% of the resources in your K8s cluster.

Overall, is a powerful messaging system that offers several advantages over Kafka. Its ability to handle high volumes of data, user-friendly interface, great cost reduction in terms of compute resources and dev time makes it a great choice for developers looking to build real-time data processing applications.


In conclusion, Kafka can be considered a message broker, as it has many of the characteristics and capabilities of a message broker. However, there are limitations to using Kafka as a message broker, and it may not be the best option for every situation. Alternative solutions such as Memphis, which is designed to run natively on Kubernetes and has additional features like message routing and transformation, may be more appropriate for certain projects. It is essential to consider the specific needs and requirements of a project when choosing a message broker. Get started with Memphis today.

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