We work and live in a time when we rely increasingly on data to get things done. Applications, services, software, mobile devices, and other elements combine to form an intricate and far-reaching web that touches and affects most areas of our lives.

As a result, there’s an increased need to handle the information flow between these different elements. Devices and apps need to talk to each other, and there is no room for error. That’s why programmers use message brokers and similar tools to exchange information and communicate with each other.

Post Graduate Program in Data Engineering

Your Gateway To Becoming a Data Engineering ExpertView Course
Post Graduate Program in Data Engineering

What’s the Difference Between a Message Broker and a Publish/Subscribe (Pub/Sub) Messaging System?

Message brokers are software modules that let applications, services, and systems communicate and exchange information. Message brokers do this by translating messages between formal messaging protocols, enabling interdependent services to directly “talk” with one another, even if they are written in different languages or running on other platforms.

Message brokers validate, route, store, and deliver messages to the designated recipients. The brokers operate as intermediaries between other applications, letting senders issue messages without knowing the consumers’ locations, whether they’re active or not, or even how many of them exist.

However, publish/Subscribe is a message distribution pattern that lets producers publish each message they want.

Data engineers and scientists refer to pub/sub as a broadcast-style distribution method, featuring a one-to-many relationship between the publisher and the consumers.

What is Kafka?

Kafka is an open-source distributed event streaming platform, facilitating raw throughput. Written in Java and Scala, Kafka is a pub/sub message bus geared towards streams and high-ingress data replay. Rather than relying on a message queue, Kafka appends messages to the log and leaves them there, where they remain until the consumer reads it or reaches its retention limit.

Kafka employs a “pull-based” approach, letting users request message batches from specific offsets. Users can leverage message batching for higher throughput and effective message delivery.

Although Kafka only ships with a Java client, it offers an adapter SDK, allowing programmers to build their unique system integration. There is also a growing catalog of community ecosystem projects and open-source clients.

Kafka was released in 2011, so it’s the newcomer. You can find a more detailed intro to Kafka here. You can also learn more about how to use it through this Kafka tutorial and look at the architecture of this pub/sub model here.

Free Course: Introduction to Data Science

Learn the Fundamentals of Data ScienceEnroll Now
Free Course: Introduction to Data Science

What is RabbitMQ?

RabbitMQ is an open-source distributed message broker that facilitates efficient message delivery in complex routing scenarios. It’s called “distributed” because RabbitMQ typically runs as a cluster of nodes where the queues are distributed across the nodes — replicated for high availability and fault tolerance.

RabbitMQ employs a push model and prevents overwhelming users via the consumer configured prefetch limit. This model is an ideal approach for low-latency messaging. It also functions well with the RabbitMQ queue-based architecture. Think of RabbitMQ as a post office, which receives, stores, and delivers mail, whereas RabbitMQ accepts, stores, and transmits binary data messages.

RabbitMQ natively implements AMQP 0.9.1 and uses plug-ins to offer additional protocols like AMQP 1.0, HTTP, STOMP, and MQTT. RabbitMQ officially supports Elixir, Go, Java, JavaScript, .NET, PHP, Python, Ruby, Objective-C, Spring, and Swift. It also supports various dev tools and clients using community plug-ins.

What is Kafka Used For?

Kafka is best used for streaming from A to B without resorting to complex routing, but with maximum throughput. It’s also ideal for event sourcing, stream processing, and carrying out modeling changes to a system as a sequence of events. Kafka is also suitable for processing data in multi-stage pipelines.

Bottom line, use Kafka if you need a framework for storing, reading, re-reading, and analyzing streaming data. It’s ideal for routinely audited systems or that store their messages permanently. Breaking it down even further, Kafka shines with real-time processing and analyzing data.

What is RabbitMQ Used For?

Developers use RabbitMQ to process high-throughput and reliable background jobs, plus integration and intercommunication between and within applications. Programmers also use RabbitMQ to perform complex routing to consumers and integrate multiple applications and services with non-trivial routing logic.

RabbitMQ is perfect for web servers that need rapid request-response. It also shares loads between workers under high load (20K+ messages/second). RabbitMQ can also handle background jobs or long-running tasks like PDF conversion, file scanning, or image scaling.

Summing it up, use RabbitMQ with long-running tasks, reliably running background jobs, and communication/integration between and within applications.

Learn Data Science with R for FREE

Master Basics of Data Science with R for FREEEnrol Now
Learn Data Science with R for FREE

Understanding the Differences Between RabbitMQ vs Kafka

These messaging frameworks approach messaging from entirely different angles, and their capabilities vary wildly. For starters, this chart breaks down some of the most significant differences.

Kafka vs RabbitMQ




4K-10K messages per second

1 million messages per second

Message Retention

Acknowledgment based

Policy-based (e.g., 30 days)

Data Type



Consumer Mode

Smart broker/dumb consumer

Dumb broker/smart consumer


Exchange type: Direct, Fan out, Topic, Header-based

Publish/subscribe based

Payload Size

No constraints

Default 1MB limit

Usage Cases

Simple use cases

Massive data/high throughput cases

More on the top differences between Kafka vs RabbitMQ:

  • Data Flow 

    RabbitMQ uses a distinct, bounded data flow. Messages are created and sent by the producer and received by the consumer. Apache Kafka uses an unbounded data flow, with the key-value pairs continuously streaming to the assigned topic.
  • Data Usage

    RabbitMQ is best for transactional data, such as order formation and placement, and user requests. Kafka works best with operational data like process operations, auditing and logging statistics, and system activity.
  • Messaging

    RabbitMQ sends messages to users. These messages are removed from the queue once they are processed and acknowledged. Kafka is a log. It uses continuous messages, which stay in the queue until the retention time expires.
  • Design Model

    RabbitMQ employs the smart broker/dumb consumer model. The broker consistently delivers messages to consumers and keeps track of their status. Kafka uses the dumb broker/smart consumer model. Kafka doesn’t monitor the messages each user has read. Rather, it retains unread messages only, preserving all messages for a set amount of time. Consumers must monitor their position in each log.
  • Topology

    RabbitMQ uses the exchange queue topology — sending messages to an exchange where they are in turn routed to various queue bindings for the consumer’s use. Kafka employs the publish/subscribe topology, sending messages across the stream to the correct topics, and then consumed by users in the different authorized groups.

Data Scientist Master's Program

In Collaboration with IBMExplore Course
Data Scientist Master's Program

Which Should You Learn in 2021 - Kafka vs RabbitMQ?

Although this may sound like a cop-out, the answer is — it depends on what your needs are. Learn and use Apache Kafka if your operation requires any of the following use cases:

  • Event sourcing or system modeling changes as a sequence of events
  • Streaming and processing data in multiple-stage pipelines
  • Applications that need a stream history, delivered in “at least once” partitioned order
  • Streams with a throughput of at least 110K/sec events, complex routing, or “at least once” partitioned ordering

And you should learn and use RabbitMQ if any of these use cases apply to your organization:

  • Granular control over consistency/set of guarantees on a per-message basis
  • Complex routing to users/consumers
  • Applications requiring a variety of publish/subscribe, or point-to-point request/reply messaging capabilities
  • Applications that must support legacy protocols, like STOMP, MQTT, AMQP, 0-9-1

If you’re uncertain where your career will take you, you may consider learning both. This strategy boosts your skillset, enhances your flexibility in a new job environment, and increases your marketability to future prospective employers. As time and resources permit, consider gaining certification in both RabbitMQ and Kafka, and be ready for anything.

When you’re ready to take the Apache Kafka certification exam, check out these Kafka practice questions first, and then you can check Kafka interview questions for preparing for the job interview.

Looking at Career Opportunities as a Data Engineer?

Data scientist and data engineer skills are among the most in-demand in 2021.  If you’re interested in a data engineering career, Simplilearn offers data engineering courses to give you a boost in the right direction. This Post Graduate Program in Data Engineering focuses on distributed processing using the Hadoop framework, large scale data processing using Spark, data pipelines with Kafka, and processing data on the AWS and Azure cloud infrastructure. Once you complete this data engineering certification course, you will be career-ready for a Data Engineering role.

Once you get the data engineering certification out of the way, consider Simplilearn’s Kafka certification training course. This course teaches you how to master architecture, installation, configuration, and Kafka open-source messaging interfaces. You will learn the basics of Apache ZooKeeper as a centralized service and develop the skills to deploy Kafka for real-time messaging.

According to Payscale.com, data engineers can earn an annual average of USD 92,325, with an upper range of around USD 132,000.

Let Simplilearn help you make your data-oriented career dreams come true. Check out the courses today, and get yourself on a career path that offers generous benefits and stability.

About the Author


Simplilearn is one of the world’s leading providers of online training for Digital Marketing, Cloud Computing, Project Management, Data Science, IT, Software Development, and many other emerging technologies.

View More
  • Disclaimer
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.