Apache Kafka Reviews

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.

• 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.

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.