Sep 04, 2023
The choice depends on your use case.
IBM MQ and Apache Kafka both compete in the field of messaging and data streaming solutions. While IBM MQ is seen as having an advantage in guaranteed message delivery and robust security, Apache Kafka leads in scalability and real-time data streaming capabilities.
Features: IBM MQ is valued for its reliable data transfer, compatibility across platforms, and stability with multiple queuing features. It guarantees message delivery and excels in integration across mixed environments. Kafka stands out in real-time data streaming and handles high volumes and velocity. It supports topic-based eventing, scalability, and has replay features through retention periods.
Room for Improvement: IBM MQ could improve its security features, user interfaces, and integration of legacy components while offering more affordable pricing and better cloud integration. Apache Kafka requires enhancements in reducing dependency on Zookeeper, simplifying the graphical interface, and improving cluster management and consumer monitoring tools.
Ease of Deployment and Customer Service: IBM MQ is appreciated for its on-premise deployment with hybrid cloud capabilities, though its customer service could improve response times and expertise. Apache Kafka benefits from open-source flexibility across various cloud structures, though it often needs third-party professional service for some features. Its growing community helps enhance support experiences.
Pricing and ROI: IBM MQ's high licensing costs make small-scale deployments challenging, but it offers reliability and performance. Its ROI stems from long-term integration. Apache Kafka's open-source nature makes it cost-effective, relying on community support. Despite the absence of licensing expenses, its ROI benefits from supporting vast distributed systems at a lower entry cost compared to IBM MQ.
It's a product which integrates the external systems with internal systems or among the systems themselves, making it an essential technology component required to integrate multiple systems.
The Apache community provides support for the open-source version.
There is plenty of community support available online.
With Microsoft, expectations are higher because we pay for a license and have a contract.
We cannot hold on to the project for a long time just to wait for IBM to fix the issues.
The response time for IBM MQ support could be better because when we are using IBM MQ and something goes wrong, support is required as the resource availability of the IBM product is very limited.
With containerized flavors of these products, we are having a tough time dealing with PMRs because the versions are new to IBM.
Customers have not faced issues with user growth or data streaming needs.
IBM MQ handles many thousands of messages in a second, indicating good scalability.
In our environment, we do not have horizontal scaling for IBM MQ, but as demand increases, we would just vertically scale it.
We've got 12 VMs running, and it's very easy to scale.
Apache Kafka is stable.
This feature of Apache Kafka has helped enhance our system stability when handling high volume data.
We have never had any downtime or crashes since it's been running.
The transaction is always guaranteed with IBM MQ, which is the main reason I have been working with it for fifteen years while dealing with financial transactions or messages.
Otherwise, they're completely stable.
The performance angle is critical, and while it works in milliseconds, the goal is to move towards microseconds.
We are always trying to find the best configs, which is a challenge.
A more user-friendly interface and better management consoles with improved documentation could be beneficial.
Having a graphical user interface would improve usability.
The pricing model for IBM MQ could be more flexible for clients.
They don't meet our standards due to the timing to get a person with knowledge.
The open-source version of Apache Kafka results in minimal costs, mainly linked to accessing documentation and limited support.
Its pricing is reasonable.
It's not cheap.
It's possible to get some training, but the cost of this learning is expensive.
The price of IBM MQ is definitely on the higher side.
Apache Kafka is effective when dealing with large volumes of data flowing at high speeds, requiring real-time processing.
Apache Kafka is particularly valuable for managing high levels of transactions.
It allows the use of data in motion, allowing data to propagate from one source to another while it is in motion.
These are financial transactions, so we do not want to lose the message at any cost.
There is a saying that for the last 30 years IBM MQ has never been hacked.
It's time-tested, very stable, highly resilient, and has all the features to troubleshoot even if something goes wrong.
| Product | Market Share (%) |
|---|---|
| Apache Kafka | 3.9% |
| Apache Flink | 14.4% |
| Databricks | 11.8% |
| Other | 69.9% |
| Product | Market Share (%) |
|---|---|
| IBM MQ | 24.7% |
| ActiveMQ | 24.2% |
| Red Hat AMQ | 9.6% |
| Other | 41.5% |
| Company Size | Count |
|---|---|
| Small Business | 32 |
| Midsize Enterprise | 18 |
| Large Enterprise | 47 |
| Company Size | Count |
|---|---|
| Small Business | 20 |
| Midsize Enterprise | 18 |
| Large Enterprise | 146 |
Apache Kafka is an open-source distributed streaming platform that serves as a central hub for handling real-time data streams. It allows efficient publishing, subscribing, and processing of data from various sources like applications, servers, and sensors.
Kafka's core benefits include high scalability for big data pipelines, fault tolerance ensuring continuous operation despite node failures, low latency for real-time applications, and decoupling of data producers from consumers.
Key features include topics for organizing data streams, producers for publishing data, consumers for subscribing to data, brokers for managing clusters, and connectors for easy integration with various data sources.
Large organizations use Kafka for real-time analytics, log aggregation, fraud detection, IoT data processing, and facilitating communication between microservices.
IBM MQ is a middleware product used to send or exchange messages across multiple platforms, including applications, systems, files, and services via MQs (messaging queues). This solution helps simplify the creation of business applications, and also makes them easier to maintain. IBM MQ is security-rich, has high performance, and provides a universal messaging backbone with robust connectivity. In addition, it also integrates easily with existing IT assets by using an SOA (service oriented architecture).
IBM MQ can be deployed:
IBM MQ supports the following APIs:
IBM MQ Features
Some of the most powerful IBM MQ features include:
IBM MQ Benefits
Some of the benefits of using IBM MQ include:
Reviews from Real Users
Below are some reviews and helpful feedback written by IBM MQ users who are currently using the solution.
PeerSpot user Sunil S., a manager at a financial services firm, explains that they never lose messages are never lost in transit, mentioning that he can store messages and forward them as required: "Whenever payments are happening, such as incoming payments to the bank, we need to notify the customer. With MQ we can actually do that asynchronously. We don't want to notify the customer for each and every payment but, rather, more like once a day. That kind of thing can be enabled with the help of MQ."
Another PeerSpot reviewer, Luis L. who is a solutions director at Thesys Technologies, says that IBM MQ is a valuable solution and is "A stable and reliable software that offers good integration between different systems."
The head of operations at a financial services firm notes that "I have found the solution to be very robust. It has a strong reputation, is easy to use, simple to configure in our enterprise software, and supports all the protocols that we use."
In addition, a Software Engineer at a financial services firm praises the security benefits of it and states that “it has the most security features I've seen in a communication solution. Security is the most important thing for our purposes."
We monitor all Streaming Analytics 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.
