Kafka vs RabbitMQ – A Head-to-Head Comparison for 2022
Kafka vs RabbitMQ – A side-by-side comparison of the performance and architectural differences between the two popular open-source messaging systems.
As a big data architect or a big data developer, when working with Microservices-based systems, you might often end up in a dilemma whether to use Apache Kafka or RabbitMQ for messaging. Rabbit MQ vs. Kafka – Which one is a better message broker?
You might find some articles across the web that conclude that Apache Kafka is better than RabbitMQ and few others that mention RabbitMQ to be more reliable than Kafka. However, if you’re here to choose between Kafka vs. RabbitMQ, we would like to tell you this might not be the right question to ask because each of these big data tools excels with its architectural features, and one can make a decision as to which is the best based on the business use case. So, if you want to find the answer to the question – Should I use RabbitMQ vs. Kafka, then we suggest you get an in-depth understanding of the two messaging systems before you decide on a message broker for your next big data project.
This article will discuss Apache Kafka and RabbitMQ messaging systems briefly to help you understand their architecture and then look at the trade-offs each of these big data tools has to offer.
Kafka vs. RabbitMQ – An Overview
But first, let’s understand the need for message brokers like Kafka and RabbitMQ. Big data engineers or developers face challenges with successful data exchange, particularly when they have to make applications interact with each other. Message brokers solve this problem of data exchange by making it reliable and simple using various protocols for messaging that show how a message has to be transmitted and consumed at the receiver.
Apache Kafka and RabbitMQ are messaging systems used in distributed computing to handle big data streams– read, write, processing, etc. They act as the message brokers between applications/services endpoints. Applications exchanging messages on the two ends can be written in a different programming language and don’t have to conform to a specific message format. Since protocol methods (messages) sent are not guaranteed to reach the peer or be successfully processed by it, both publishers and consumers need a mechanism for delivery and processing confirmation.
What is RabbitMQ?
With over 10K+ users, RabbitMQ is one of the most widely deployed message brokers that help applications and services exchange information with each other without maintaining homogeneous exchange protocols. Like in most protocol methods, messages sent do not guarantee that they’ve been delivered and processed, so RabbitMQ adopts message delivery acknowledgment and implements smart broker design in its architecture. A smart broker is one that provides messages to consumers by handling the processing at its side. The consumer needn’t worry about asking for data. Consumers fundamentally act as dummy recipients of the information. Data is promptly deleted from RabbitMQ as soon as consumers have finished processing it.
What is Kafka?
Kafka is a commit-log/messages-processing implementation that stresses data storage and retrieval more, with scalability and data redundancy. It implements an intelligent client/subscriber policy where the consumer is solely responsible for requesting and retrieving data from the broker. Messages can be processed in batches or individually from the broker and can be re-requested multiple times for processing after that.
Kafka vs. RabbitMQ -What’s the difference?
Compare? What parameters… wait, isn’t comparison the killer of joy?
Why Kafka vs RabbitMQ?
Apache Kafka and RabbitMQ are equally excellent and veracious when put against in comparison as messaging systems. RabbitMQ vs. Kafka discussion isn’t about which is better among the two but which messaging system is ideal for a given business use case.
This article originally appeared on www.projectpro.io/. To read the full article, click here.
Nastel Technologies is the global leader in Integration Infrastructure Management (i2M). It helps companies achieve flawless delivery of digital services powered by integration infrastructure by delivering tools for Middleware Management, Monitoring, Tracking, and Analytics to detect anomalies, accelerate decisions, and enable customers to constantly innovate, to answer business-centric questions, and provide actionable guidance for decision-makers. It is particularly focused on IBM MQ, Apache Kafka, Solace, TIBCO EMS, ACE/IIB and also supports RabbitMQ, ActiveMQ, Blockchain, IOT, DataPower, MFT, IBM Cloud Pak for Integration and many more.
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