Apache Kafka Reviews

We have used Kafka for streaming customer web clicks from live sessions to understand customer behavioral patterns.

The ability to partition data on Kafka is valuable. But Kafka needs support and management. It is better to have it fully managed on the cloud.

The only reason I give Kafka as product a low rating is because there are far superior and cheaper alternatives in cloud-based solutions, where we save money on manpower, electricity, servers, datacenters, networking, etc.

In fact, this is the view I have for pretty much all open source software compared to cloud based services. They just make things cheaper, faster, scalable and manageable. Kafka is good, but Kafka as a cloud service is awesome!!

This is a relative rating (compared to cloud services), not that something is wrong with Kafka. I hope that is clear.

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).

No issues here with stability.

Ah, scalability!!! We need to set up multiple servers again for handling the load, which makes Kafka not scalable, unless you subscribe to cloud services.

It’s an Apache-community based support, so it is not really prioritized if you have a business issue. This is why most enterprise customers pay for cloud services.

We didn’t have a previous solution. We started with Kafka and then switched to Amazon Kinesis (PaaS for Kafka). I think Microsoft Azure also released a competing service.

The setup was straightforward.

Licensing issues are not applicable. Apache licensing makes it simple with almost zero cost for the software itself.

We unsuccessfully, and kind of foolishly, tried Apache Camel. They were not similar in services, so we moved to Kafka rightfully, and then to AWS cloud ultimately.

If you have a dedicated Kafka resource to implement and manage the services, then go for Apache Kafka. Otherwise, do consider cloud-based services from AWS or Azure.

I like Kafka's flexibility, stability, reliability, and robustness.

Kafka has a lot of monitors, but sometimes it's most important to just have a simple monitor. Improvements to Kafka's management would be nice, but it's not so necessary for me. There are a lot of consoles that offer a better view than Kafka. Some are free, and some are paid, but I'm thinking about streaming. For example, if you connect more streams to a component in the same queue, how will it integrate to recognize the flow and the message?

I've been using Kafka for more than two years.

Kafka is stable. Defining our user base is hard because Kafka influences the whole company, so you could say around 100 users. Kafka is a core system, so it affects all users we choose to link to the primary key.

I previously used IBM MQ. 

The first time we tried to deploy Kafka, it seemed a little complicated, but the second try went better. Sometimes it isn't easy to set up the necessary communication or estimate how many partitions we need. Some applications have a vast amount of data, so we have to consider how to improve the performance and not increase the transfer times.

I rate Apache Kafka nine out of 10. I think it's one of the best tools on the internet.

On-premises

I am an enterprise architect involved in Big Data and integration projects using Apache Kafa. We use it for integrating our different management systems.

The most important feature for me is the guaranteed delivery of messages from producers to consumers.

More adapters for connecting to different systems need to be available.

I have been using Kafka for about six months.

This is a stable solution and we haven't had any complexities.

This solution is scalable.

I have used IBM MQ and it is better in terms of the adapters that are available. However, the price of IBM MQ is very high.

The initial setup is easy.

Kafka is more reasonably priced than IBM MQ.

Although we are deployed on-premises at the moment, we are looking to have a cloud-based deployment in a year or two.

This is a solution that I can recommend but it will take a lot of time to develop the adapters.

I would rate this solution a seven out of ten.

On-premises

Excellent speeds for publishing messages faster.

Too much dependency on the zookeeper and leader selection is still the bottleneck for Kafka implementation.

RESTful API implementation actually uses the Kafka Broker to publish the messages but I am not able to find it becoming scalable. Partially, the reason might be there is no load balancer for the RESTful API web server.

Setup is very much straightforward for development, and cluster setup is also easy. I am not aware of the production setup yet.

I implemented it on my own.

We are a solution provider and Apache Kafka is being used in our client's company.

The most valuable feature is the support for a high volume of data.

The initial setup and deployment could be less complex.

Integration is one of the main concerns that we have.

We have been using Apache Kafka for two years.

Kafka is a stable product.

This is a scalable solution.

The technical support is quite good, and we have no problem with it.

We also use IBM MQ. It is also a stable product.

IBM MQ is probably easier to deploy than Kafka.

In addition to these, I have also worked with RabbitMQ.

Deploying Kafka is more complex than IBM MQ.

My customer has asked me to choose between IBM MQ and Apache Kafka. I will be comparing these two solutions in the near future. My impression is that Kafka is going to better suit my customer, but I have to consider their specific needs before I can be sure.

This is a solution that I may recommend, but its suitability depends on the needs and requirements.

I would rate this solution an eight out of ten.

On-premises

• Replication, partitioning, and reliability are the most valuable features.
• Even if one of my clusters fails, the replication factor of a topic makes sure that I have the data available for processing, so I won't lose any of it.
• Partitioning enables me to process the parallel requests. It helps in reaching the throughput.

One improvement is in regards to the OS memory management. In case there are too many partitions, it runs into memory issues. Although this is a very rare scenario, it can happen.

I have been using this product for a year now.

There were no stability issues.

Kafka is a highly scalable product. We have not faced any scalability issues so far.

Since it's an open source product, no technical support is available. However, the open source community is very active.

The initial setup was straightforward. Just go through the Kafka documentation and it will be up and running in no time.

Since it's an open source product, there is no pricing for it.

We used Apache Kafka to receive information in streams for Telco projects. As customers bought airtime, we received that information via Apache Kafka streams.

There is a thin line between a normal queuing system, such as ActiveMQ or RabbitMQ, and a streaming system like Apache Kafka. Apache Kafka is effective when dealing with large volumes of data flowing at high speeds, requiring real-time processing. It was useful for us in receiving constant recharge information for customers. Queuing systems, however, excel at providing acknowledgment of messages, which is not a feature of streaming systems.

I haven't explored its features extensively enough to suggest improvements. A more user-friendly interface and better management consoles with improved documentation could be beneficial.

We used Apache Kafka for about a year. The project is on hold now, so we are not actively using it.

We did not face significant challenges while integrating Apache Kafka with our existing systems. The implementation was straightforward.

I have never had to use the technical support as Apache Kafka is a very open-source system. There is plenty of community support available online.

We switched from using API gateways to an in-house integration platform. However, before Apache Kafka, we used RabbitMQ.

The initial setup took approximately a couple of weeks. The system installation was already existing, and we just had to tie into a specific topic.

We haven't seen a return on investment with Apache Kafka. It's used for a specific use case rather than cost reduction.

Its pricing is reasonable. It's not always about cost, but about meeting specific needs.

RabbitMQ is not a direct competitor to Apache Kafka, as both have their specific domains and use cases.

It is crucial to understand your use case before deciding on a solution. While I can replace RabbitMQ with Apache Kafka, acknowledgment of messages in queuing systems makes them preferable for certain applications like building a payment gateway system. I would rate Apache Kafka seven or eight out of ten.

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