Apache Kafka vs. IBM MQ

For brokering messages, Confluent Kafka is well suited since it offers a managed solution ready to use. Scenarios where the solution is not very well suited are for example, where pricing is an issue. The solution costs quite a lot for basic usage (for example: for 3 clusters, pricing is above 100k$ a year).

Incentivized

Suited:

• As we have critical data such as payments which needs to be managed across a number of platforms.
• MQ ensures guaranteed delivery of this data.
• Setting up a new application to use MQ is a relatively simple process.
• To track data both internally and externally you can set MQ up to send back acknowledgement . Very useful if one party say they have not received some data.

Not suited.

• If an application has a requirement to get information from a database without any concerns of losing data . Using JDBC connecting directly to the database can reduces the number of jumps the application needs to make to get to the database.

• If an application wants to store information and then send as a large batch file it is not recommended to use MQ. MQ applications should be used for data which needs to be sent immediately. MFT (MQ file transfer) however can utilise MQ for file transfers

Incentivized

• Apache Kafka is able to handle a large number of I/Os (writes) using 3-4 cheap servers.
• It scales very well over large workloads and can handle extreme-scale deployments (eg. Linkedin with 300 billion user events each day).
• The same Kafka setup can be used as a messaging bus, storage system or a log aggregator making it easy to maintain as one system feeding multiple applications.

Incentivized

• The documentation is very clear,It is understandable and the support helps to configure it in the best way.
• Server guidelines make it possible to get the most out of work management. It's broad, we can work with different operating systems, I really recommend using linux.
• It is highly compatible with systems, brockers, applications, and data accumulation programs, it is possible to configure everything so that after the installation of programs, they can communicate with each other and then throw data to an external program that accumulates it and represents in clear details of steps to follow and make business decisions.

Incentivized

• The Kafka Tool is a community-made Java application that looks and feels from the past century.
• Logging can be confusing. This certainly shows when we have to do troubleshooting.
• Hybrid scenarios - pub/sub, but there are services in and outside a Kubernetes cluster. Then there are a ~3 options, but only 2 (the harder ones) are production-safe.

Incentivized

• I can only speak wonders about the program; I think it is a program with enough serious track record to meet the company's expectations. However, it should be noted that I could suggest that promotional packages be made from time to time for those of us who are already clients of more than one year.
• In every sense, the program fulfills what it promises, which is to generate a good connection and cohesion of programs to be able to make commitments between them.

Incentivized

Kafka has suited our use case very well so far. Going forward we are planning to expand our platform manifold so the load on Kafka and our reliance on Kafka is going to increase only.

Apache Kafka is highly recommended to develop loosely coupled, real-time processing applications. Also, Apache Kafka provides property based configuration. Producer, Consumer and broker contain their own separate property file

Incentivized

I give it a nine because it has significantly improved my team's data reliability and operational efficiency. Its great security features give us peace of mind, knowing our sensitive data is well protected. While the setup might initially be complex, I believe the long-term benefits far outweigh this hurdle.

Incentivized

As I have said before, the program is stable; I think that is the great reason why it has been maintained for years and days in the company; despite the hard use that we have given it, it has behaved well during both day and night shifts.

Incentivized

Support for Apache Kafka (if willing to pay) is available from Confluent that includes the same time that created Kafka at Linkedin so they know this software in and out. Moreover, Apache Kafka is well known and best practices documents and deployment scenarios are easily available for download. For example, from eBay, Linkedin, Uber, and NYTimes.

Incentivized

The IBM Support team has offered unmatched insight. Their personnel helped us actualize several things that seemed impossible before. You see, previously we would develop a software solution, but it would take months before rolling it out mostly because we had further build additional specific interfaces. The support team suggested we procure App Connect alongside MQ. Right now, we just hop into the IBM MQ platform, connect the software with AppConnect, and a few modifications are done, then it's ready within a few days.

Incentivized

Apache Kafka is built for scale. From high throughput and real-time data streaming, it has a strong advantage over RabbitMQ with its low latency. This put Apache Kafka at the forefront as the platform of choice for large datasets messaging and ensuring scalability when data scale up tremendously. RabbitMQ however has its strengths in traditional messaging. Routing and message delivery reliability are the bedrock of RabbitMQ and this is where RabbitMQ excels. In my previous workplace, RabbitMQ was of choice as reliability matters more than scale. In two words. Apache Kafka for scale, RabbitMQ for reliability. And for cloud deployment and large dataset messaging in what I am doing now, Apache Kafka is the default choice.

Kafka is renowned for its impressive throughput, fault tolerance, and real-time data streaming capabilities. Nonetheless, IBM MQ remains the preferred choice due to its unwavering commitment to guaranteed delivery and exceptional reliability. Fault-Tolerant Architectures of IBM MQ which allows active-standby queue managers, to build fault-tolerant architectures that ensure continuity of service in the event of hardware or software failures.

Incentivized

• Positive: bursts of traffic on special holidays are easy to handle because Kafka can absorb and buffer all the messages we need to process long enough to let an understaffed set of back-end services catch up on processing. Hard to put a number to it but we probably save $5k a month having fewer machines running.
• Positive: makes decoupling the web and API services from the deeper back-end services easier by providing topics as an interface. This allowed us to split up our teams and have them develop independently of each other, speeding up software development.
• Negative: our engineers have made mistakes such as accidentally dropping a few thousand messages due to the CLI being confusing to use, and as a result a customer lost some of their precious data. I'd say that was more our fault than Kafka's though.

Incentivized

• Positive- Message Reliability and Reduced downtime, increases the ROI many times.
• Positive- Increased stability and enhanced customer experience
• Negative- cost is very high - Both licensing and integration cost
• Negative- Learning and training cost of IBM MQ is high as its complex to use and integrate

Incentivized