Apache Kafka Reviews

Apache Kafka is used for connecting components between each other in the same application. The use is quite limited, but I was curious about its filtering capability of it.

We implemented the notification system between our components, and we found that Apache Kafka performs well in scalability. It has improved our organization because of the scalability and the comfort of a fail-safe or disaster recovery it provides.

Apache Kafka can improve by making the documentation more user-friendly. It would be beneficial if we could explain to customers in more detail how the solution operates but the documentation get highly technical quickly. For example, if they had a simple page where we can show the customers how it works without the need for the customer to have a computer science background.

I have been using Apache Kafka for approximately two years.

Apache Kafka is scalable. It is easy to add brokers.

We have approximately 30 people using this solution in my organization. They use the solution daily.

I have only used Apache Kafka.

The initial setup of Apache Kafka took some time but after it was easy.

I rate the initial setup of Apache Kafka a three out of five.

We set up the solution in-house.

This is an open-source solution and is free to use.

We have not used the solution in production. We do not have a lot of data at the moment.

I would recommend this solution to others.

I rate Apache Kafka an eight out of ten.

Apache Kafka is used for stream processing, metric and log aggregations, and as a message queue for connecting different microservices.

The stream processing is a very valuable aspect of the solution for us.

Due to the fact that the solution is open source, it has a zookeeper dependency. If I could change anything about the solution, it would be that.

The solution could always add a few more features to enhance its usage.

I've been with the company for at least one year, which is for how long I've been using the solution.

The stability of the solution is very good, even for large enterprise-level organizations. It's quite reliable. There aren't bugs or glitches that affect it. The solution doesn't crash.

The solution is scalable, however, it's a 50/50 endeavor. It may require some management to build it out.

The solution is open source, so there isn't technical support per se. The open-source community that surrounds the technology, however, is very good.

That said, our company provides technical support to our clients if they need it. It's 24/7 support and we try to reply within 20 minutes of receiving a request.

Some of our clients are using Apache, while others are using other solutions. It depends on the company and its unique requirements.

The difficulty or simplicity of the initial setup varies. It really depends on the organization and its requirements and infrastructure.

Deployment times vary. It can be up to a week in production, however, with some products online, some services can be deployed within minutes.

When you have already deployed the solution, and it's installed, it doesn't require very much maintenance. If it needs any, my company handles it for our clients. We have an entire team that can work on it.

The solution is open source; it's free to use.

What happens in our company is a little different. We basically provide services to other companies through Kafka, like our management services. It doesn't necessarily mean we're using the solution ourselves, however, we will be going and deploying Kafka for companies, like a systems integrator.

The version of the solution is normally 2.4, however, it depends on the requirements. Our cloud providers are always different due to the fact that the countries that we work with are all different. For example, in the US it could Amazon, Azure, or Google. It varies.

I'd advise other organizations considering using the solution to make sure they understand what the use case is. They need to know what their services will be and if they will be directed to Apache Kafka.

From a customer perspective, potential companies need to make sure they have an idea of how big it's going to be due to the fact that it's a cluster environment. It needs to be taken care of. Customers will need to know things like what is the message rate is which is coming into Kafka and how they will connect all those different microservices or any services together to Kafka.

From an infrastructure perspective, it's more of how big of a cluster a company needs. Who would be the producers to produce it, and who's the consumer who's consuming the data are a few questions that need to be asked.

I'd rate the solution eight out of ten.

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.