Apache Kafka Reviews

We are building solutions on Apache Kafka for four customers. The customers we have are in various sectors, such as healthcare and architecture.

Apache Kafka has good integration capabilities and has plenty of adapters in its ecosystem if you want to build something. There are adapters for many platforms, such as Java, Azure, and Microsoft's ecosystem. Other solutions, such as Pulsar have fewer adapters available.

I have been using Apache Kafka for three years.

Apache Kafka is stable.

I would recommend Apache Kafka for any enterprise.

The amount of people using the solution depends on the application. However, the starting point is from 6,000 to 7,000 concurrent users.

There is not any support, Apache Kafka is open-source.

We have been experimenting with other solutions such as VMware RabbitMQ and Pulsar.

We are going to replace the Apache Kafka solution using Pulsar.

Pulsar gives more scalability to an even grouping, but Apache Kafka is used more if you want to send something in a time series-based. If this does not matter to you then Pulsar could be more customizable. Apache Kafka is nothing but a streaming system with local storage. Apache Kafka fits into many use cases, it's very direct, but if you want more specific use cases and you use Apache Kafka, Pulsar could be considered.

Apache Kafka was simple to install. If you have a complicated clustered production, it takes time. However, for the development, it doesn't take more than one or two hours.

We have approximately two to four technical managers that are deploying and supporting Apache Kafka. A technical manager is necessary.

Apache Kafka is an open-sourced solution. There are fees if you want the support, and I would recommend it for enterprises. There are annual subscriptions available.

Apache Kafka is one of the best open-source solutions that are available today.

I would recommend this solution to others.

I rate Apache Kafka an eight out of ten.

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 deployed this solution in a project for one of our customers to synchronize the different applications; to transport information from one application to another. I'm a program manager and we are customers of Apache.

This solution is robust and delivers messages quickly. It's a simple but good product.

The management tool could be improved.

I've been using this solution for the past few months. 

In this project, we used Kafka to synchronize 28 nodes spread out nationally and it seems scalable. We plan to consolidate the 28 nodes for national integration of nodes and schemas.

We didn't need to contact technical support. We just allotted the software, installed it and started working with it. We carried out a lot of testing pre-development. Development was done with a company that previously used Kafka so we were able to exchange technical information. 

We previously used ActiveMQ for another project.

The initial setup was a little complex. We carried out the development ourselves. 

Our clients purchased the license and they think it's an affordable solution. 

I recommend this solution, we're probably going to use it again in another project.

I rate this solution eight out of 10. 

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.

We are in the early stages of testing this solution in our lab as a demo. It is in development and we are not in production at this point.

We are using this solution to relay events when they happen to multiple receivers at once to allow better functionality.

Apache Kafka has helped our client's online restaurant company by allowing them to take any orders and send the notifications with some other details, such as logic commands, to the different microservices.

The valuable features are the group community and support.

The solution can improve by having automation for developers. We have done many manual calculations and it has been difficult but if it was automated it would be much better.

I have been using this solution for approximately three months.

The solution's scalability is important for our ability to have more throughput from multiple receivers. If we need more throughput it can deliver.

We did use other solutions previously but this solution makes things a lot easier.

The installation is fairly easy. Additionally, there is a cloud-based version available if a use case requires it.

We did the implementation ourselves.

The solution is free, it is open-source.

There is a lot of configuration involved in this solution. We have found many configurations that have helped us but it would be beneficial if there was automation. 

I rate Apache Kafka a five out of ten.