Apache Kafka vs. IBM MQ

As of March 2019, Apache Kafka is ranked 3rd in Message Queue with 14 reviews vs IBM MQ which is ranked 1st in Message Queue with 63 reviews. The top reviewer of Apache Kafka writes "It offers throughput with built-in fault-tolerance and replication". The top reviewer of IBM MQ writes "I like that the ability to add applications to it is simple". Apache Kafka is most compared with IBM MQ, ActiveMQ and Solace PubSub+. IBM MQ is most compared with RabbitMQ, ActiveMQ and Apache Kafka. See our Apache Kafka vs. IBM MQ report.
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Most Helpful Review
Find out what your peers are saying about Apache Kafka vs. IBM MQ and other solutions. Updated: March 2019.
325,421 professionals have used our research since 2012.
Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

It eases our current data flow and framework.I like the performance and reliability of Kafka. I needed a data streaming buffer that could handle thousands of messages per second with at least one processing point for an analytics pipeline. Kafka fits this requirement very well.The ability to partition data on Kafka is valuable.Kafka, as compared with other messaging system options, is great for large scale message processing applications. It offers high throughput with built-in fault-tolerance and replication.Apache Kafka is actually a distributed commit log. That is different than most messaging and queuing systems before it.Ease of use.Excellent speeds for publishing messages faster.

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It improves reliability and guarantees that messages are not lost.Reliable integration between MQ servers is the most valuable feature.Data integrity, reliability and security are valuable features that IBM MQ possesses.There is no dependency on the end party service's run status.We use queue managers/concentrators for message flow going upstream and downstream on applications with enterprise licenses.It runs everywhere, from the mainframe in the US to the PCs in the Gobi desert attached to an analog modem.Has helped integrate between applications, reduce rework, and costs by reusing working components of existing applications.Integrates between distributed systems: For example, it can help integrate processing between mainframe, client-server, web-based applications by integrating the messages, supporting Service Oriented Architecture.

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Kafka 2.0 has been released for over a month, and I wanted to try out the new features. However, the configuration is a little bit complicated: Kafka Broker, Kafka Manager, ZooKeeper Servers, etc.As an open-source project, Kafka is still fairly young and has not yet built out the stability and features that other open-source projects have acquired over the many years. If done correctly, Kafka can also take over the stream-processing space that technologies such as Apache Storm cover.The product is good, but it needs implementation and on-going support. The whole cloud engagement model has made the adoption of Kafka better due to PaaS (Amazon Kinesis, a fully managed service by AWS).Kafka requires non-trivial expertise with DevOps to deploy in production at scale. The organization needs to understand ZooKeeper and Kafka and should consider using additional tools, such as MirrorMaker, so that the organization can survive an availability zone or a region going down.The GUI tools for monitoring and support are still very basic and not very rich. There is no help in determining a shard key for performance.Stability of the API and the technical support could be improved.Too much dependency on the zookeeper and leader selection is still the bottleneck for Kafka implementation.

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I believe there is too much code to be done in order to handle the elements that you develop.I believe the stability of the product has decreased since we began using it initially.MQ needs instruments for connection with new modern queues like Kafka or RabbitMQ.SonicMQ CAA (continuous availability architecture) functionality on auto failover and data persistence should be made available without a shared drive, as it exists in multi-instance queue managers.It could get a face lift with a modern marketing campaign.the level of training as well as product marketing for this product are not that great. You rarely find a good training institute that provides training. Many of the architects in several organization are neither aware of the product nor interested in using it. IBM should provide good training on products like this.It needs a User Interface which is better than the aging MQ Explorer. The existing solution MQ Explorer is outdated.The installation of product upgrades and patches is very difficult. It requires the use of the IBM Installation Manager (IM).

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Pricing and Cost Advice
Licensing issues are not applicable. Apache licensing makes it simple with almost zero cost for the software itself.It is open source software.When starting to look at a distributed message system, look for a cloud solution first. It is an easier entry point than an on-premises hardware solution.I would not subscribe to the Confluent platform, but rather stay on the free open source version. The extra cost wasn't justified.

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To implement such an IBM solution, a company has to pay a lot in term of licensing and consultancy. A pricing model might be a better option.In terms of cost, IBM MQ is slightly on the higher side.IBM MQ appliance has pricing options, but they are costly.99.999 percent availability for less than a penny per message over the past 25 years. IBM MQ is the cheapest software in the IBM software portfolio, and it is one of the best.Pricing could be better, as with all IBM products. But their performance in production, along with security and scalability, will pay returns in the long run.I think the pricing is reasonable, especially with IIB as a part of it.Use the new and lightweight version (Liberty) to lower licensing costs. It is also easier to upgrade/maintain.IBM MQ has a flexible license model based on the Processor Value Unit (PVU) and I recommend it.

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Top Comparisons
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Also Known As
WebSphere MQ

Apache Kafka is a distributed streaming platform, with the following capabilities:

  • It lets you publish and subscribe to streams of records. In this respect it is similar to a message queue or enterprise messaging system.
  • It lets you store streams of records in a fault-tolerant way.
  • It lets you process streams of records as they occur.

Apache Kafka gets used for two broad classes of application:

  • Building real-time streaming data pipelines that reliably get data between systems or applications.
  • Building real-time streaming applications that transform or react to the streams of data.

    IBM MQ provides the universal messaging backbone for service-oriented architecture (SOA) connectivity. It connects virtually any commercial IT system, whether on premise, in the cloud, or a mixture. For more than 20 years IBM has led the market in messaging middleware and more than 10,000 businesses across all geographies and industries rely on IBM MQ.

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Learn more about Apache Kafka
Learn more about IBM MQ
Sample Customers
Information Not Available
Deutsche Bahn, Bon-Ton, WestJet, ARBURG, Northern Territory Government, Tata Steel Europe, Sharp Corporation
Top Industries
Marketing Services Firm14%
Logistics Company14%
Financial Services Firm14%
Financial Services Firm45%
Engineering Company7%
Healthcare Company6%
Financial Services Firm29%
Insurance Company15%
Healthcare Company8%
Financial Services Firm34%
Insurance Company17%
Engineering Company8%
Company Size
Small Business29%
Midsize Enterprise10%
Large Enterprise62%
Small Business2%
Midsize Enterprise10%
Large Enterprise88%
Small Business11%
Midsize Enterprise3%
Large Enterprise86%
Find out what your peers are saying about Apache Kafka vs. IBM MQ and other solutions. Updated: March 2019.
325,421 professionals have used our research since 2012.
We monitor all Message Queue reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.

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