Apache Kafka Reviews

We use Apache Kafka for our messaging. 

We publish a message and ask the subscriber to listen to it. We use it to save events generated by integration with external systems. There are external events, that are first published to our Kafka queue, and then to a topic, and then we save it to our own data storage system.

I believe that the speed, and especially the performance, are very good features.

Also included is a cluster with built-in failover. 

It is the performance that is really meaningful.

In terms of features, we are satisfied. I don't require any additional features. I don't believe we require any additional features at this time.

More Windows support, I believe, is one area where it can improve. We need to wrap it as a service, but there isn't one built into Windows. So that's something they could improve.

I believe Windows Server is primarily aimed at the Windows shop or those who use Windows.

I don't recall the specific version that we are using, it may be Kafka 2.11, but it is not the latest one.

It's stable. However, the Windows Service is not very stable because it is a wrapper.

We are a small team with a few people.

We might increase our usage in the future.

We don't get in touch with technical support. We rely on open-source software. We haven't used the help of technical support. We did not seek assistance. As a result, I have no opinion on the subject.

This is the first product we have used. We didn't have anything prior to that.

The initial setup is straightforward. It's easy to set up.

It took a few days to get it up and running.

We only need one or two engineers to keep this solution running. We basically let it run and monitor what's going on. We usually don't touch it unless something goes wrong.

We deployed it ourselves.

It's free. We use the free version.

VMware RabbitMQ and ActiveMQ are products that are not being used by us. I wanted to look into it. But we use different things.

We compared our findings to those of other researchers. We are primarily concerned with performance. Kafka is unquestionably the performance leader.

I would recommend trying this solution, but you should probably run it on Linux.

I like this product, I would rate Apache Kafka a nine out of ten.

In my previous company, we had a proprietary implementation and we changed it with Kafka. We changed it because we had many different connectors available and it also allowed us to create a window to our products for the client. It was an on-premise product and it allowed the outline to take the data out, without us developing anything.

You can connect in any language and there are a lot of connectors available, it helps a lot. And it creates visibility into the data and stability. There are several alternatives but this is one of the best options for this.

It's very easy to install and it's pretty stable.

The possibility to have connectors is very helpful. Another valuable aspect is that it's mature and open-source. 

From a scalability point of view, you just add servers and it's scalable. The whole architecture is very scalable.

There is a feature that we're currently using called MirrorMaker. We use it to combine the information from different Kafka servers into another server. It's very wide and it gives a very generic scenario. I think it would be great if the possibility would exist out of the box and not as a third party. The third party is not very stable and sometimes you have problems with this component. There are some developments in newer versions and we're about to try them out, but I'm not sure if it closes the gap.

I have been using this solution for six months. I also worked with it additionally in my previous company but not so intensively. 

I have never needed to use technical support. I know it's available but we haven't needed it because there's a lot of information on the internet that has helped us to solve our issues. 

I would definitely recommend Kafka. In our current position, we use it to move a lot of data and I think it's definitely working well. I would definitely recommend it.

I would rate it an eight out of ten. 

Our primary use case for this solution is streaming.

We have been using this solution for four years.

The solution is stable. However, it's not possible to substitute IBM MQ with Apache Kafka because the JMS part is not very stable. It is inadequate and doesn't have the support of the MQI interface of IBM MQ.

The solution is scalable. Deployment is speedy, but we don't have many installations. We have over a thousand users using this solution and will most likely increase the number of users because we have tested 100,000 messages per second. The solution is impressive.

We previously used Mosquitto and Rabbit solutions, but we currently use Apache Kafka.

We are licensed annually for this solution.

I rate this solution a nine out of ten for streaming. I recommend it to other people. The solution is good, but its performance can be improved.

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.

We get amazing throughput. We don't get any delay. 

The manageability should be improved. There are lots of things we need to manage and it should have a function that enables us to manage them all cohesively. There should be a default property. It's really hard to manage all these things.

I have been using Apache Kafka for around six months. 

It is really stable. The stability is really good. I just installed it on my local machine. 

It is scalable. 

I use it 24/7. We have to change both packets to Kafka and from Kafka it receives data. We retain the packet for only six seconds.

I haven't needed to contact their customer support. It's quite simple. I didn't need to contact them to manage my application. 

It's wasn't so easy for me to deploy. 

My advice would be to go through the documents and understand the topics. Learn what its effects are and take care of partitioning. 

Based on my experience, I would rate it an eight out of ten. It's quite complicated and the configuration requires a lot of effort. As a developer it is quite hard to go into all these things.

I'm a software architect. The use case will depend my customers. They usually use it for data transfer from static files to a legacy system.

In the next release, I would like for there to be some authorization features and HTL security. 

We also need bigger software and better monitoring. 

I have been using Apache Kafka for the last ten years. 

The stability is good. We've never had any issues. 

Scalability is very good. 

I have never needed to contact technical support. My colleagues get support from here, in Morrocco. 

The setup is not a big deal for us. We can handle it. After the system is set up, the person who administers it has to do so with Apache Kafka.

Depending on the setup, it will usually take two weeks.

I would rate it a nine out of ten. Not a ten because of the monitoring and admin improvement I'd like for them to make. 

The most valuable features are performance, persistent messaging, and reliability. It allows us to persist the message for a configurable number of days, even after it has been delivered to the consumer. The message delivery is also fast.

We wanted to track the customer activities on our application and store those details on another system(RDBMS/Apache Hadoop). We do extensive analysis with that. This helps the company to analyze the customer activities, such as search terms, and do better.

It’s perfect for our requirements.

I have been using Apache Kafka for two years.

We have had no issues with stability.

We have had no issues with scalability.

We use the open source one, so we did not opt for any technical support.

We started to use Apache Kafka with our application from scratch.

The initial setup was straightforward. We faced some issues during the development in areas such as message producer and consumer. We rectified those with the tweaking the producer and consumer configurations. The documentation is very good.

I don’t have any idea, as we use the open source version.

It's a high-performance distributed system. If you want to track the user activities or any stream processing, then this is perfect. We have used Docker Kafka for our implementation. It's very easy for setup and testing. You could also try the same.

The most valuable features to me are replication, partitioning and easy integration with Apache Spark, which we use quite a bit for distributed processing.

Replication is good for high availability. It provides additional safety for data in case of node failure or data center outage. Partitioning is a really useful feature for parallelizing processing. We use Apache Spark to process data from a Kafka queue, and Spark is able to assign one executor to each Kafka partition. The more partitions we have, the more threads we can use to process data in parallel. This helps us achieve really good throughput.

It will help us build a scalable platform. This will allow the company to provide better customer service.

It’s pretty easy to use for now. I haven’t had any difficulty or problems that I can complain about. Maybe they can add a UI to the configure queues and to display statistics about data stores.

I have used Kafka for about a year.

So far, we have not encountered any stability issues.

We have not had any scalability issues. The product is horizontally scalable, so adding extra hardware is all that is needed.

We haven’t needed technical support with the product yet.

I think performance-wise, the product is very good and fits in our use case. We used other distributed message queues, but all products have their own use case

Initial setup wasn’t really complex. We use Kafka through Hortonworks Suite, which comes with many other big data tools. Ambari makes it easy to setup

Licensing and pricing was handled by my management, so I don’t have much knowledge there.

Give it a try. It’s a valuable, high-performance, distributed processing tool.