Apache Kafka Vs RabbitMQ: What are the Main Differences?

For smooth communication between devices and applications, read this tutorial to compare Apache Kafka Vs RabbitMQ for your selection:

We are living in an era where feeding the existing data to various software systems has increased to a great extent. The association between applications, services, software, mobile devices, and various other fundamentals has built a distantly dispersed web that influences a major part of our daily life.

This indeed has resulted in the management of data flow between these fundamentals to a greater level.

Communication between devices and applications should happen without glitches, which has created a need to use Message Brokers and Publish/Subscribe messaging systems.

Apache Kafka Vs RabbitMQ – Comparison

Apache Kafka Vs RabbitMQ

Now you must be wondering what Message Brokers and Publish/Subscribe messaging systems are. Here is a brief description of these two.

Message Brokers are nothing but a software unit that allows Applications, Services, and devices to communicate and exchange information with each other. RabbitMQ is an example of a Message Broker system.

In contrast to this, Publish/Subscribe is a message distribution pattern that allows producers to publish each message they want. Apache Kafka is an example of Publish/Subscribe system.

Apache Kafka is a freeware dispersed event streaming podium, hereby promoting unprocessed throughput. Kafka allows applications to alter, persist, and re-alter data that was already streamed. Apache Kafka was built by LinkedIn in 2011 and later given to Apache foundation as an open source software.


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RabbitMQ is a freeware dispersed message broker which enables seamless message delivery in convoluted routing cases. This is best suited for cases like Online Payment processing.

In contrast to Apache Kafka, RabbitMQ is an oldie, as it was built in 2007. RabbitMQ was a major component in SOA systems. However, nowadays it is being used for streaming purposes as well.

Now let’s try to understand the architecture of these two products.

RabbitMQ Architecture


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  • The Producer pushes the message to an exchange. An exchange comprises Direct, Topic, and Fan-out.
  • The message from the exchange is further sent to the Queue or other exchanges.
  • The Consumer reads messages from the queue, generally, there will be a fixed limit of messages.
  • The Queue in RabbitMQ is sequential and follows FIFO (First in, First out). However, the FIFO order is not assured in case of priority and shared queues.

Kafka Architecture


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Producers send the content to the clusters, which in turn is sent to the Consumers via the pull feature. Here, each server node in the cluster is a broker which carries the data sent by the producers until it reaches the consumer.

Kafka makes sure that the message is ordered within the partition in a round-robin fashion, but not beyond partitions in a topic. Producers can add the key to a message. So all messages with the same key will go to the same partition.


Apache Kafka is developed in JAVA and Scala. That is the reason Apache Kafka only comes with a JAVA client. However, it provides an adapter SDK through which we can build exclusive system integration.

RabbitMQ is written in Erlang. It has drivers/clients available for almost all major languages.

Suggested Reading =>> Hands-on Java Tutorial Series for Beginners


Consumers in Apache Kafka get distributed via partitions in topics. Each consumer receives messages from a particular partition at a time.

In contrast to Apache Kafka, for every queue in RabbitMQ, there are several consumers. These are called Competitive consumers, as competition happens between these consumers and others for the consumption of messages.

Design Model

There are many differences in the design model of Apache Kafka and RabbitMQ. Apache Kafka has a dumb broker/smart consumer model, which does not monitor each message that the user has read. However, it keeps the unread messages only hereby conserving all messages for a set amount of time.

On the contrary, RabbitMQ has the smart broker/dumb consumer model, which constantly delivers messages to consumers and keeps track of their status.

High Availability

When it comes to availability, both Apache Kafka and RabbitMQ have their plus points.

Apache Kafka with the support of Zookeeper administers the state of the cluster and provides high availability.

RabbitMQ with its greatly available queues and clustering gives high-performance data cloning, which in turn gives high availability.

Protocols of a Message

Apache Kafka uses primeval protocols like int8, int16, etc. along with binary messages, whereas RabbitMQ makes use of standard chain protocols like STOMP, AMQP, and HTTP.


In Apache Kafka, replicated brokers are readily available which come into action when the master broker is down.

In RabbitMQ, the natural replication of queues never happens. Configuration to replicate the queue is a must.

Arrangement of Message

The arrangement of Messages in Kafka happens interior to the partition. It makes sure either all pass together or it will fail.

In RabbitMQ, the order for flow happens through an individual AMQP channel. Also, inside the queue logic, reordering of the packets which are transmitted again happens which will make sure that consumers cannot alter the sequence.

Life of a Message

Since messages get appended at the end of the log file of Apache Kafka. We can retrieve messages from it anytime.

On the contrary, RabbitMQ has a queue. Therefore, messages once received by the consumers are deleted and acknowledgment is received.

Multi Subscriber

In Apache Kafka, more than one consumer type can subscribe to many messages. However, in RabbitMQ, since messages are routed to various queues, only one consumer from the queue can process the message.


Only those transactions happen in Apache Kafka which shows the read, process, and write patterns done to or from the topics of Kafka.

In RabbitMQ, atomicity is not assured when the transaction is happening in an individual queue.

Use cases

Apache Kafka Use Cases:

  • Apache Kafka can be used for various high-volume, high-throughput exercise monitoring, like consuming data from IoT sensors and keeping track of shipments.
  • It can develop application logic based on a stream of events.
  • Apache Kafka can be used for gathering logs from various services and produce them for their various customers in a standard format.
  • Since Apache Kafka supports the gathering of large chunks of log data, it becomes a very essential component for any Event Management System that comprises Security Information Event Management. Administering a huge chunk of log data makes it a brilliant backend for developing an application.
  • We can also use it for data replication among the nodes and to restore data on failed nodes.
  • Apache Kafka is a Publish-Subscribe message system, which allows it to be used for reading and writing data more easily.

RabbitMQ Use Cases:

  • RabbitMQ can route messages between many consuming applications like in a microservices architecture.
  • Companies like Softonic, a File sharing company, use RabbitMQ in the file scanning process. The customer uploads a file to the site but before the file is shared with the other user, all the files are validated and scanned for viruses. In the system, file scanning requests are added to RabbitMQ. File scanning services can handle requests one by one or in batches.
  • Imagine a web application that creates pdfs for its customers. So, the system collects information from the customer, generates a pdf, and emails it back to them. Handling the information, generating the pdfs, and sending the emails takes several seconds. So, in this application, a generated pdf request is placed on the RabbitMQ queue. The consumer then receives a message from the queue and processes the pdf and at the same time, the producer can queue up new messages to RabbitMQ.
  • We can use RabbitMQ to scale financial applications.

Pull Vs Push Approach

Apache Kafka is based on the Pull model. Batches of messages are requested by consumers from a specific offset. Systems having pull-based models have some shortcomings, like wasting resources due to polling at regular intervals. Apache Kafka supports long polling waiting till real data comes through.

Actually, looking at the architecture of Apache Kafka where partitions are present, the pull-based approach makes the right choice. As there is no competitor in the partition, Apache Kafka gives a message to order. This will allow the user to take the benefit of message batching for more effective message delivery and higher throughput.

RabbitMQ is based on the Push model. The data here is sent from broker to consumer. Generally, push-based models are used for low-latency messaging.

Push based model of RabbitMQ limits the consumer from being informed of any message comeback. The broker here guarantees the delivery of the message to the consumer by sending an acknowledgment after processing the data.

In case of a negative response, another message was sent which was added to the queue.

Security and Operations

Apache Kafka and RabbitMQ have built-in tools for providing security and operations. In addition to that, both provide third-party tools as well that increase tracking metrics from nodes, clusters, and queues.

The evolution of Kubernetes in today’s times lets infrastructure drivers run both Apache Kafka and RabbitMQ on Kubernetes.

Apache Kafka depends on Transport layer security(TLS), and Java Authentication and Authorization Service(JAAS). In contrast to this, RabbitMQ has a browser-based API.

Both Apache Kafka and RabbitMQ hold role-based access controls and Simple Authentication and Security Layer Authentication(SASL). However, one plus point for Apache Kafka is that we can manage security policies through a command line interface.

Comparison Table: Apache Kafka Vs RabbitMQ

Let us know to try to look at the differences between Apache Kafka and RabbitMQ via the comparison table: 

Apache Kafka vs RabbitMQApache KafkaRabbitMQ
Performance1 million messages per second4K-10K messages per second
Message retentionPolicy basedAcknowledgement based
Data TypeOperationalTransactional
Consumer modeDumb broker/smart consumerSmart broker/ dumb consumer
Payload sizeDefault 1MB limitNo constraints
Use casesHigh throughput casesLow latency cases
TopologyPublish/subscribe basedExchange type
Message orderingProvided through its partitioningNot supported
Message prioritiesUnavailableCan be set

Which one you should learn in 2022?

Learning is not something that you can finish, it will always go from one chapter to the other. Learning Apache Kafka or RabbitMQ will depend upon what your requirements are.

If your application requires the following use cases, Learn and use Apache Kafka:

  • Event sourcing is a sequence of events.
  • Need to access stream history and direct stream processing. Apache Kafka’s append-only log will allow the developer to do this.
  • If your application requires more effective message delivery and higher throughput.
  • If your application requires you to read and write data more easily.

If your application requires the following use cases, Learn and use RabbitMQ:

  • If an application requires complex routing to consumers.
  • If an application requires micro-level control over message delivery.
  • If the application is supporting legacy protocols like STOMP, MQTT, AMQP.

Career opportunities and Pay scales as Data Engineer:

The third millennium (Starting from 2020) has marked a boom for Data Engineers.

Today, almost every system needs data to be fed into it so that it can be used for various purposes like Analytics, Performance, Sales, Finance, etc.

There are several platforms in the market that offer great learning in Data Science. There are many postgraduate programs in Data Engineering that cover Apache Kafka, Hadoop framework, and large-scale data processing. Data Engineer skills are the most appealing skills for almost every organization.

A candidate with a Data Engineer Certification can easily pull off an average of USD 92,325 with an upper range of around USD 132,000.

Frequently Asked Questions

Q#1) When to use Apache Kafka vs RabbitMQ?

Answer: Apache Kafka provides append-only logs. So when developers need to access stream history and directly process the stream, Kafka has to be used. If you need to do event sourcing as a sequence of events, Kafka can be the best choice.

For scaling the financial firms, we can use RabbitMQ.

Q#2) Apache Kafka Vs RabbitMQ – What is the main difference?

Answer: Apache Kafka is known for streaming from A to B without resorting to complex routing, whereas RabbitMQ performs complex routing to consumers.

Q#3) Can Apache Kafka and RabbitMQ be set up on Kubernetes?

Answer: Apache Kafka and RabbitMQ both can be set up on Kubernetes.

Also Read =>> Comparison between Kubernetes Vs Docker

Q#4) Apache Kafka, RabbitMQ – Can we use them for microservices?

Answer: Apache Kafka uses a high-yield routing approach while RabbitMQ is used for the fastest response time from the server. It depends on what the user is but both Apache Kafka and RabbitMQ can be used for microservices.

Q#5) Apache Kafka Vs RabbitMQ – Which has higher performance?

Answer: Both Apache Kafka and RabbitMQ are advanced in their performance which cannot be quantitatively measured. Apache Kafka is known for its high throughput, RabbitMQ shines with low-latency message delivery.


The above are some notable key differences for Apache Kafka Vs RabbitMQ. Still it is very crucial to say one is better than the other. Both Apache Kafka and RabbitMQ are high-performing tools that can be used based on the type of requirement.

Apache Kafka is best known for its high throughput whereas RabbitMQ is best suitable for low-latency message delivery and complex routing as well.

Despite these differences, they both share some common use cases as well like, both can be used for microservices, and both can be deployed on Kubernetes.

Both Apache Kafka and RabbitMQ can handle a large number of messages. We can only say, make a wise choice keeping the requirement, and using cases in mind.