6 Best Kafka Alternatives: 2024’s Must-know List

It’s no secret that Kafka was the first event-streaming platform that helped organizations effectively harness the power of real-time data. Over the years, it has become the go-to solution for handling and processing streaming data for quick decision-making. Despite its widespread adoption, Kafka may not always be the best choice for message queuing. Kafka requires a lot of Memory and Storage, so setting up a multi-node cluster for testing purposes on a standalone workstation can be tricky. Organizations and users are thus shifting to Kafka Alternatives that are more user-friendly.

Prerequisites

• A basic understanding of data streaming infrastructure 

Understanding Kafka 

Kafka is an Open-Source software program that lets you store, read, and analyze streaming data. It is free for everyone to use and is supported by a large community of users and developers who consistently contribute to new features, updates, and support. As a “Distributed” system, Kafka can run on several servers to leverage different servers’ processing power and storage capacity.

As a part of its original design, Kafka was intended to monitor the behavior of users on websites. A complete picture of user behavior can be obtained by analyzing clickstream data after every session (where do users navigate on the website and what functions they use). Using this information, you can predict which articles or products a visitor would be interested in.

To further extend its capabilities, Apache Kafka supports integrations with Apache Flume, Apache Spark Streaming, Apache Storm, and HBase for real-time research, analysis, and streaming data processing to extend its capabilities further.

Key Features of Kafka 

Let us dive deep into some of the key features of Apache Kafka.

• Scalability: Using Kafka, you can scale in all four dimensions: Event Producer, Event Processor, Event Consumer, and Event Connector without facing any downtime.
• Persistent for Short Run: The retention policy of Kafka is, by default, seven days, which can be changed as per user. Using Kafka, you can store your data for Short Periods before erasing the oldest values.
• Replication: Kafka MirrorMaker supports cluster replication. Multiple Data Centers or Cloud Regions can receive messages via replication features. 
• Reliability and Durability: Kafka maintains a replica of its messages on disk, allowing intra-cluster replication. This makes Kafka a highly durable messaging system. 
• Extensibility: Since Kafka can be plugged into several applications, you can seamlessly add new functionality and execute your initiatives.

Tabular Difference

	Type	Scaling	Protocol Support
Kafka	Distributed Event Streaming Platform	Horizontal scaling with partitions	Kafka Protocol
Kinesis	Real Time Data Streaming Service	Automatic scaling	Kinesis Protocol
Rabbit MQ	Message Broker	Vertical and Horizontal scaling	AMQP, MQTT, STOMP, HTTP
Active MQ	Message Broker	Horizontal scaling with broker clusters	JMS, AMQP, MQTT, STOMP, HTTP
Red Hat	Message Broker	Horizontal scaling with broker clusters	AMQP, MQTT, JMS, STOMP, WebSocket
IBM MQ	Message Broker	Horizontal scaling with clustering	JMS, AMQP, MQTT, HTTP
Amazon SQS	Real-Time Data Streaming Service	Auto-scaling based on usage	HTTP, HTTPS

Top 6 Kafka Alternatives

Kafka is a widely used publish-subscribe-messaging service known for managing large volumes of information, handling both online and offline messages. However, Kafka has some shortcomings such as slow speeds, message tweaking, lesser message paradigms, and more, thereby increasing the usage of Kafka Alternatives.

Below is a comprehensive list of top Kafka Alternatives that can be used to manage real-time data feeds while maintaining low latency and high throughput:

1) Amazon Kinesis 

Amazon Kinesis consists of services that let you process real-time streaming data. It provides a platform that is scalable, allowing it to collect, process, and analyze high volumes of data in real-time. This suite includes Kinesis Data Streams for real-time data ingestion, Kinesis Data Firehose for delivering data into destinations such as S3 and Redshift, and Kinesis Data Analytics for real-time analytics using SQL. This integration would support end-to-end data processing pipelines, providing applications such as log and event data processing, real-time metrics, and data transformation.

Key Features

• It facilitates real-time data ingestion and processing.
• It allows seamless interoperability between other AWS services, such as Lambda, and Redshift.
• It has a scalable, fully managed infrastructure with inbuilt fault tolerance.

Pricing

Amazon Kinesis pricing is based on the amount of data ingested, processed, and stored, with separate costs for data streams, shards, and data retrieval. For more details on their pricing plans, you can visit the Amazon Kinesis Pricing page.

Advantages over Kafka

Let us discuss how Amazon Kinesis can overcome the shortcomings of Apache Kafka.

• Operational Overhead: Kafka requires excess operational effort to set up, manage, and scale clusters. Whereas Kinesis is a fully managed service and it does not require any manual configurations and set up.
• Integration with Ecosystem: Integrating Kafka with non-Kafka ecosystems often requires additional tools and configurations. Kinesis, on the other hand, can easily integrate with AWS services.
• Scalability: Scaling Kafks requires manual intervention to resize the clusters. Meanwhile, Kinesis automatically scales up or down according to your requirements.

Limitations

• Since pricing is based on data processed and stored, it can become costly while scaling up.
• It is limited to AWS services integrations, and performing configurations to non-AWS ecosystems can be complex.

2) RabbitMQ

RabbitMQ is an open-source message broker that allows communication between applications through message queuing. It supports multiple messaging protocols and offers robust features for message delivery and routing.

Key Features

• It supports flexible messaging patterns, like pub/sub, request/reply, and routing.
• It Integrates seamlessly with multiple protocols such as AMQP, MQTT, and STOMP.
• It provides high availability and fault tolerance via clustering and replication.

Pricing

RabbitMQ itself is open-source and free to use.

Advantages over Kafka

• Diverse Messaging Patterns: RabbitMQ supports numerous patterns in messaging and protocols. This makes the system more flexible for various use cases.
• Ease of Setup: It is generally easier to set up for simpler use cases than the complex configurations required in Kafka.
• Flexible Retry Mechanism: It Offers built-in features for handling message retries through dead-letter exchanges (DLXs) and configurable retry policies. On the other hand, additional infrastructure is required in Kafka to handle message retries.

Limitations

• During very high message rates or large-scale deployments, its performance can slow down.
• It requires careful tuning and configuration to achieve high throughput.
• It has limited built-in features for data processing as compared to other streaming platforms.

3) ActiveMQ

ActiveMQ is a flexible, open-source, multi-protocol messaging broker that supports many protocols. This makes it easy for developers to use a variety of languages and platforms. The AMQP protocol facilitates integration with many applications based on different platforms.

Key Features

• It offers features like master-slave configurations and a network of brokers for fault tolerance to safeguard the messages in case of failure.
• ActiveMQ is completely compliant with the Java Message Service specification. It implements a wide range of advanced JMS features, including message selectors, durable topic subscriptions, and transactions.
• It comes with the ActiveMQ Artemis broker, which is a very high-performance, next-generation messaging broker with advanced features such as very high throughput, low latency, and an even more modern architecture.

Pricing

ActiveMQ is an open-source, free-to-use tool. You do not have to pay to use it.

Advantages over Kafka

Let us discuss the advantages of ActiveMQ that make it better than Kafka.

• Message Flexibility: It provides advanced messaging features, including message priorities, delayed delivery, and a variety of message acknowledgment strategies. On the other hand, Kafka primarily focuses on high-throughput data streams.
• Embedded Brokers: It can be embedded within Java applications as an in-process broker. Kafka, on the other hand, runs as an external distributed system and does not offer an embedded broker option.

Limitations

• Scaling ActiveMQ can be more complex than other messaging systems.
• Managing ActiveMQ can be complex for large-scale deployments.
• It can face performance issues when dealing with vast amounts of data.

4) Red Hat AMQ

With a robust suite of components based on communities like Apache Kafka and ActiveMQ, Red Hat AMQ offers a secure and lightweight solution message delivery and one of the best Kafka Alternatives. Compared to most streaming tools, Red Hat AMQ has faster execution and offers a flexible messaging tool that allows instant communication. Consequently, Red Hat AMQ effectively meets organizational needs and integrates business apps across organizations.

Key Features

• It comes with enterprise-grade support, certification, and compliance to various industry standards, which gives the most ideal solution for critical applications.
• It provides a single console for managing and monitoring all kinds of messaging components, from ActiveMQ to Apache Camel.

Pricing

Red Hat AMQ has two subscription plans: 

• 16-Core-Standard – This plan costs $31,680
• 16-Core-Premium – This plan costs $47,520

Get more details on the pricing plans.

Advantages over Kafka

• Easy Enterprise Solution Integration: It offers smooth integration with Red Hat enterprise tools and platforms, easing deployments and management in an environment hooked on solution interconnectivity.
• Rich Messaging Features: It provides robust support for traditional messaging features such as JMS, notably message priorities and durable subscriptions, which are not natively supported within Kafka’s stream-oriented model.

Limitations

• It is higher in cost compared to open-source competitors owing to its subscription fees for support and updates.
• It is tightly integrated with Red Hat’s ecosystem, which may limit flexibility if you are using non-Red Hat infrastructure.

5) IBM MQ

IBM MQ provides enterprise-level messaging capabilities, allowing secure and efficient communication between applications. The system provides seamless communication across projects, efficiently handling multiple transactions. It retains the one-and-only-once delivery pattern, thereby ensuring Security.

Key Features

• It has an easy-to-use interface.
• It provides high reliability and data security.
• It allows interoperability between various applications, either within or outside organizations.

Pricing

It offers three pricing models:

• IBM MQ
• IBM MQ Advanced
• IBM MQ on Cloud/Saas

To know more about the pricing plans of IBM MQ.

Advantages over Kafka

• Enterprise Grade Security: Security is one of the important functionalities provided by IBM MQ. It has inbuilt encryption with full authentication and authorization mechanisms, most of which are stronger than what is available with Kafka for security.
• Transactional Support: It provides very good support for transactional messaging, reliable processing, and message delivery to the right destinations.

Limitations

• It can be complex to configure and manage, particularly in large or highly distributed environments.
• It may require significant system resources and infrastructure to fully utilize its capabilities.

6) Amazon SQS

Amazon SQS (Simple Queue Service) is a messaging queueing service that is fully managed for Distributed Systems, Serverless Applications, and Microservices. It is known for the separation of components and the development of asynchronous processes. With its excellent SDK and functional console, it is favored by developers.

Key Features

• It is fully managed with automatic scaling, high availability, and fault tolerance, eliminating the need for infrastructure management.
• It  offers Standard Queues for high-throughput, distributed systems, and FIFO Queues for guaranteed order and exactly-once processing.

Pricing

Amazon SQS pricing is based on the number of requests and the amount of data transferred.

Advantages over Kafka

Let us discuss some of its key advantages over Kafka.

• Fully Managed Service: Unlike Kafka, which requires setup and management, Amazon SQS is an AWS fully managed service, meaning that users need not have anything to do with the infrastructure, scaling, or maintenance.
• Cost Effective: For simple queuing scenarios, SQS could become more cost-effective because of the pay-as-you-go pricing and lack of overhead for cluster management.

Limitations

• This limits messages to 256 KB, so larger payloads will need additional handling.
• SQS is a queuing service and does not provide native capabilities for real-time stream processing or complex event processing.

Conclusion

In this blog, we have explained the key aspects of Kafka and indicated a few important alternatives. While Kafka remains one of the greatest solutions for stream processing, the fast evolution of technology and the different needs within gave rise to new generations of solutions that fill the gaps left by Kafka. 

Now, with the demand for real-time data and event-driven architectures on the rise, organizations have a variety of new-generation stream processing technologies at their fingertips. Such alternatives could be reviewed with an emphasis on solutions customized for specific use cases, enabling the handling of real-time data in a more effective and efficient way.

FAQs about Kafka Alternatives

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