Apache Kafka Reviews

We use Apache Kafka primarily to queue the transactions or total the transactions.

Apache Kafka has helped our organization handle larger volumes without affecting the infrastructure load.

All the features of Apache Kafka are valuable, I cannot single out one feature.

Prioritization of messages in Apache Kafka could improve.

I have been using Apache Kafka for approximately six years.

The stability of Apache Kafka is very good.

Apache Kafka is the most scalable solution in the market.

I have not used the support from Apache Kafka.

Apache Kafka is straightforward to implement.

We did the implementation of Apache Kafka in-house.

I did not evaluate other solutions.

I rate Apache Kafka a nine out of ten.

The most valuable feature is the documentation, which is good and clear.

An area for improvement would be growth.

I've been using this solution for just over a year.

Kafka works very well.

The initial setup was simple.

I would rate this solution six out of ten.

This is a system for email and other small devices. There has been a relay of transactions continuously over the last two years it has been in production.

Besides better stability and scalability, there are no additional functionalities I'd like to see. Kafka is good at what it does.

The management overhead is more compared to the messaging system. There are challenges here and there. Like for long usage, it requires restarts and nodes from time to time.

We started using this solution two years ago.

There are issues with stability. It's not 100% stable like ActiveMQ, but it's maybe 98% stable.

With the containerized version we have used, we have faced challenges with the scalability.

Initial setup was not easy. It requires intermediate skills.

This is an open-source version.

I would rate this solution 8 out of 10.

We deploy it for our customers. The main use case is related to log management and metrics because we are a partner of Elastic Stack, and we usually collect information through Kafka.

The high availability is valuable. It is robust, and we can rely on it for a huge amount of data. 

The Kafka Streams capability is also valuable. We get many options to build applications on top of Kafka.

The price for the enterprise version is quite high. It would be better to have a lower price.

I have been working with this solution for four or five years.

It is absolutely stable.

It is very scalable. It is easy to scale it. 

It doesn't matter how many users are using it. The licenses are calculated based on the number of nodes. It is not based on the number of users who are using it. We have between 10 to 20 nodes on average in an organization.

It is quite good, but they don't speak Italian. In Italy, we have to provide support in the Italian language. It is a problem for customers to have support in English. This is the reason why we provide direct support to customers.

I am into pre-sales and project management. I don't usually install Apache Kafka, but its basic installation seems quite simple.

Its deployment is usually quite short. Usually, we are able to deploy it in a few days, but data management and application development can take a few months.

We have our own team to deploy it. We also take care of its maintenance. We have a team of five or six employees to provide 24/7 support to our customers.

It depends on the project. For log management projects, the ROI is not very quick, but we have other projects where we used Kafka for high-value applications, and the ROI was very quick. We got an ROI in a few months.

The price for the enterprise version is quite high.

For on-premise, there is an annual fee, which starts at 60,000 euros, but it is usually higher than 100,000 euros. The cost for a project including the subscription is usually between 100,000 to 200,000 euros. The cost also depends on the level of support. There are two different levels of support.

Kafka is a really good product. To be able to keep it running in the long term, you need to know very well how it works. You should have good knowledge about it. It isn't about just knowing how to install it because it is quite simple to install it. It is important to have the right knowledge and experience to do a good installation and let it run for a long period. You can also go for someone who has the right experience and knowledge.

We are very satisfied with Kafka. I would rate it an eight out of 10. It is not perfect, but it is a really good product.

I have a lot of messages, and we need to process those messages from many clients. Each client takes those messages and processes them.

I'm using the brokerage partner. I'm not storing or maintaining the application on servers. I'm just a client for the Apache Kafka server.

The solution is deployed on-prem.

Apache Kafka has improved our organization because it's more reliable than Rabbit. That's the whole point for us.

The most valuable feature is the messaging function and reliability.

Something that could be improved is having an interface to monitor the consuming rate. We use something, but I'm not sure if it's from Apache Kafka, or if it's a borrowed third-party solution. So, the interface for monitoring the processes is an additional feature that could be added.

I have been using this solution for two years.

The solution is pretty stable compared to Rabbit or other brokers. 

The solution is scalable. We have about 10 departments that use Kafka in various forms. Each department might have 5 or 10 people.

We use the solution all the time. We have consumers that consume messages that come every day because we have clients and customers for the main website. All of those messages go to KAF clients. Our backend departments consume messages from the actions of the final customers.

We used Rabbit and we switched to Kafka because it seemed like an upgrade in ability, reliability, and in the consuming process of broker messages.

Implementations took half a year for everyone to learn the solution. It was quite lengthy.

I would rate this solution 9 out of 10.

My advice is to take some time in investigating how to implement the solution.

We used to require about half a year to implement in our organization. Someone who needs to implement Kafka has to be prepared for a quite lengthy process. Don't expect implementation to be completed in a week. It's a little bit longer because it's complex.

It's a combination of an on-premise and cloud deployment. We use AWS, and we have our offshore deployment that's on-premise for OpenShift, Red Hat, and Kafka. Red Hat provides managed services and everything. We use Kafka and a specific deployment where we deploy on our basic VMs and consume Kafka as well.

We publish or stream all our business events as well as some of the technical events. You stream it out to Kafka, and multiple consumers develop a different set of solutions. It could be reporting, analytics, or even some data persistence. Later, we used it to build a data lake solution. They all would be consuming the data or events we are streaming into Kafka.

With Kafka, events and streaming are persistent, and multiple subscribers can consume the data. This is an advantage of Kafka compared to simple queue-based solutions.

We are still on the production aspect, with our service provider or hyper-scalers providing the solutions. I would like to see some improvement on the HA and DR solutions, where everything is happening in real-time. 

Kafka's interface could also use some work. Some of our products are in C, and we don't have any libraries to use with C. From an interface perspective, we had a library from the readies. And we are streaming some of the products we built to readies. That is one of the requirements. It would be good to have those libraries available in a future release for our C++ clients or public libraries, so we can include them in our product and build on that.

We've been using Apache Kafka for the past two to three years.

Kafka is stable. It's a great product. 

We did some benchmarking, but we are still looking further to scale up some of the benchmarking and performances. So far, it meets all our business requirements. We are just developers, so everything goes to the clients, who will deploy it at their scale and use it for their end customers. So were are looking at it from a developer's perspective. Those who are developing the products are working on this.

We haven't really contacted technical support, but some of our clients have subscribed to support from the vendors. We generally look for open-source solutions. From there, we try to figure out if there are any issues. There's a good online community where you can ask questions.

We were able to deploy and use it with no problems for our use case. We didn't find it so complex. We work with so many applications, databases, Postgres, and so many other things, so we could manage it easily. We deployed Kafka in a few hours. We have an infrastructure team and DevOps. Those teams are pretty capable, and they've completely automated the whole deployment. It always takes time the first time you upgrade any application, not just Kafka. We might discover some issues, such as configuration, parameters, compatibility, etc. Once that becomes standard, it is stable, and then they only need to replicate it to the different environments or different developers groups. We have a sophisticated process.

I rate Apache Kafka eight out of 10. There are so many products on the market, so my advice is to consider if Kafka suits your business requirements first. If it's suitable, the next step is to check whether all the technical requirements are met. If everything checks out, I would say that Kafka is a relatively stable, sound, and scalable product, so they can try it out. 

We primarily use the solution for upstreaming messages with different payload for our applications ranging from iOT, Food delivery and patient monitoring. 

For example for one solution we have a real-time location finding, whereby a customer for the food delivery solution wants to know, where his or her order is on a map. The delivery person's mobile phone would start publishing its location to Kafka, and then Kafka processes it, and then publishes it to subscribers, or, in this case, the customer. It allows them to see information in real-time almost instantly.

Apache Kafka has became our main component on almost all our distributed solutions. It has helped us to delivery fast distributing messages to our customer's applications.

The solution is good for publishing transactions for commercial solutions whereby a duplicate will not affect any part of the system.

The solution is very easy to set up.

The stability is very good.

There's an online community available that can help answer questions or troubleshoot problems. 

The scalability of Kafka is very good.

It provides high throughput.

Kafka can allow for duplicates, which isn't as helpful in some of our scenarios. They need to work on their duplicate management capabilities but for now developers should ensure idempotent operations for such scenarios.

While the solution scales well and easily, you need to understand your future needs and prep for the peaks. 

I've been using the solution for four years so far.

The stability is excellent. There are no bugs or glitches. It doesn't crash or freeze. It's reliable. 

Scaling is not really a problem with Kafka. We have used Kubernetes clusters and it is working very well. It scales up and down, almost automatically almost unnoticeable to the consumers, based upon our configuration. Kafka is just one pod inside of our cluster that scales horizontally.

We have a couple of customers that also have vertical scaling, meaning that, there's more CPU, more memory available to the Kafka pod.

For Kafka, we don't actually require support from the company. We usually have people experienced in-house and sometimes we just ask in the community. 

The initial setup is easy. The majority of the tools today are really very easy to configure and setup. Docker Containers and Kubernetes, actually, have made life easier for architects as well as developers.

Nowadays, you just install the container, and then you don't have to really manage the internals at libraries, OS levels, et cetera. You just run the container. Everything is containerized.

Apache Kafka is OpenSource, you can set it up in your own Kubernetes cluster or subscribe to Kafka providers online as a service.

New users should understand the product capabilities. Often, people will start putting their hands in new products without knowing the capabilities and the disadvantages in specific scenarios. In our case for example, We haven't used Kafka for financial transaction processing, for which we still use IBM MQ, but It really depends upon your knowledge and experience with the product. My advice is to understand the product very well, its pros and cons and work from there.

Finally I'd rate the solution at a nine out of ten.

One of our clients needed to take events out of SAP to stream them through Apache Kafka while applying data enrichment before reaching the consumers.

The solution can handle more speed and has horizontal scalability for both messaging, but more specifically stream processing and data enrichment. By using this solution it can reduce the number of components required in the tech stack. For example, we were taking data events out of SAP and sending them to consumers without having to go through multiple processors that were outside of the KAFKA space. Additionally, we are using Kafka from GoldenGate to propagate database updates in real-time.

The most valuable features are the stream API, consumer groups, and the way that the scaling takes place. 

I would like to see real-time event-based consumption of messages rather than the traditional way through a loop. The traditional messaging system works by listing and looping with a small wait to check to see what the messages are. A push system is where you have something that is ready to receive a message and when the message comes in and hits the partition, it goes straight to the consumer versus the consumer having to pull. I believe this consumer approach is something they are working on and may come in an upcoming release. However, that is message consumption versus message listening.

Confluent created the KSQL language, but they gave it to the open-source community. I would like to see KSQL be able to be used on raw data versus structured and semi-structured data.

I have been using this solution for approximately one year.

The solution is stable.

I have found the Apache Kafka to be highly scalable

The project we were working on was open-source, we were using Confluent as support and they were great.

Apache Kafka on AWS is a bit complex. There is a third-party company called Confluent and they have the support that makes their installation much easier, especially for the on-premise deployment. You install Apache Kafka alone it can be a little complex compared to other queuing messaging solutions.

The on-premise deployment takes approximately a few days. The cloud or hybrid deployments including all the permissions, typologies, firewalls, and networking configuration can take weeks for all the accessibility issues to be resolved. However, the delay could have been client-related and not necessarily the solution.

We provide the implementation service.

Apache Kafka is free. My clients were using Confluent which provides high-quality support and services, and it was relatively expensive for our client. There was a lot of back and forth on negotiating the price.

Confluent has an offering that has Cloud-Based pricing. There are different packages, prices, and capabilities. The highest level being the most expensive. AWS provides services to their market, for example, to have Kafka running. I do not know what the pricing is and I am fairly confident, Azure and GCP provide similar services.

My advice to others wanting to implement this solution is to start with data streaming projects, not simple messaging projects because while it is very good at general-purpose messaging, it is more suited and geared for when you are using it as a streaming solution.

I rate Apache Kafka an eight out of ten.