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.

We use Apache Kafka to ingest a lot of data in real time that Apache Spark processes, and the result is used for a tech decision in real time – in the IT environment, infrastructure environment, and IOT environment, like for a  manufacturing plant.

This is an open-source framework. We also sell professional services on this solution and specifically create a business application for customers. 

The application is called Sherlogic. We have two kinds of customers. We have end-user customers that use the Sherlogic solution, and maybe customers don't know that there is Spark and Kafka in Sherlogic. But we have another kind of customer that uses professional services by Xautomata to create tailor-made applications in analytics and the automation process.

We use Apache Kafka for our digital cloud.

To store a large set of analytical data we are using SQL repository. This type of repository works very well but we need specific and high maintenance. The user experience is friendly.

We are looking for alternative solutions, we tried with noSQL solutions and Confluent specific features but the results were not satisfactory both in terms of performance and usability.

We are working on automated SQL repository management and maintenance tools in order to increase the democratization of our platform.

We've been using this solution for a year and a half.

The stability is very nice. We currently manage 50 million events daily.

It's scalable.

Support is good. It's typical for an open source application. You can have all the information in a public portal. If you want specific consulting, there is a company that promotes this consulting worldwide called Conduent. Their consulting is quick and they have a lot of know-how.

It's very complex, like Spark. 

Deployment took 50 minutes for all the Kubernetes ports, Spark, Kafka, and other components based on Sherlogic. In 30 minutes, we created an environment using this program to make installation easier.

Deployment was done in-house, but we're starting a collaboration with another company and we introduced this company to running this solution. Specifically, we started a collaboration with AWS to promote our platform in a Western marketplace. In this way, it's very easy to use our solution because it is a part of an AWS service, certificated by an engineer.

The return on investment has been having people dedicated to this solution because it's open source so it hasn't been necessary to invest in licensing or pay a fee. So, internal know-how has been the ROI.

It's a bit cheaper compared to other Q applications.

I would rate this solution 7 out of 10.

I would recommend this solution because the queue manager is very fast and stable.

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 use Apache Kafka for financial purposes. Every time one of our subscribed customers is due for an insurance payment, Apache Kafka sends an automated notification to the customer to let them know that their bill is due.

Resiliency is great and also the fact that it handles different data formats. There is one data format that's universal across multiple application domains — Avro. It's pretty universal compared to JSON, XML, SQI, and other formats.

Some vendors don't offer extra features for monitoring. Some come with Linux for default monitoring. Monitoring is very important. If something is not working properly, then our subscribers won't receive a notification. You then have to trace it back to Kafka and find the glitch or the messaging sequence that hasn't been racked up correctly.

It should support Avro — which handles different data formats — as a default data format. It would be much more flexible if it did.

I have been using Apache Kafka for three years.

It seems to be quite stable.

Apache Kafka is Scalable. You can actually launch a server node or a broker. Three nodes and Zookeeper (the Kafka server management system) is optimal. If one of them goes down you can automatically launch another one. You can go three servers or brokers back — there's a repetition on each Kafka broker.

Apache Kafka is open-source. They don't offer technical support.

On a scale from one to ten, I would give Apache Kafka a rating of eight.

We are a service implementer and we supply this solution to our customers. I'm a company co-founder and we are customers of Apache. 

The solution has improved our functionality, it's one of the best streaming platforms I've used. 

I'd like to see improvement in terms of observability.

I've been using this product for the last five years on and off. 

The solution is stable. 

The solution is scalable.

The initial setup is straightforward, it's not complicated.

This is an open-source product.

I rate this solution nine out of 10. 

We had an application stack consisting of Salesforce frontend and a Commander VPN position management system and used Apache Kafka to decouple the microservices. Additionally, we planned to use Kafka for stream processing and to use event sourcing to pull data from legacy systems and reference data to form a compacted topic that the microservices could consume.

The usage of Kafka is a combination of deploying on a personal Kubernetes cluster or using a managed service such as MSK. However, most people who use Kafka are using a managed service provided by Confluent. It can be deployed on the cloud or on-premise.

The most valuable feature of Apache Kafka is its versatility. It can solve many use cases or can be a part of many use cases. Its fundamental value of it is in the real-time processing capability.

You need time-sensitive technology now, particularly in the analytics space. We have looked at using change data capture and Apache Kafka to modernize our analytics capabilities. Additionally, microservices can be used to capture events from legacy systems.

Managing Apache Kafka can be a challenge, but there are solutions. I used the newest release, as it seems they have removed Zookeeper, which should make it easier. Confluent provides a fully managed Kafka platform, in which the cluster does not need to be managed.

If it is a native Apache Kafka, it would have schema registry capabilities. However, this type of functionality is often provided by third-party tools. Additionally, there may be a need for improved manageability and additional tools to manage the cluster, including standard operational metrics and inbuilt management capabilities.

I have been using Apache Kafka for approximately three years.

The solution is highly resilient.

I rate the stability of Apache Kafka a nine out of ten.

Apache Kafka is scalable.

I rate the scalability of Apache Kafka a nine out of ten.

The support from Apache Kafka is good.

The initial setup of Apache Kafka is easy to set up a cluster.  I did the initial setup on my laptop and it is straightforward. I used the Confluent version, but even if you want to run native capabilities it's straightforward to do the implementation.

The recent proof of concept was done on behalf of a client by a system integrator. Similarly, the previous one was mainly done in-house and it utilized Confluent, Apache Kafka, and MSK. The process involved setting up pre-built capabilities.

The price of the solution is low.

I rate the price of Apache Kafka a nine out of ten.

I rate Apache Kafka a nine out of ten.

We use an open-source version of this solution, and we have two deployments of it. One is on-prem, and the other is in the cloud. We use the on-prem version to aggregate our logs. We use the cloud version to manage queues for financial services. 

It just works and it's super fast. We were struggling with a Rabbit MQ cluster, so the Apache cluster is way easier.

I would like them to reduce the learning curve around the creation of brokers and topics. They also need to improve on the concept of the partitions. 

As for features, RabbitMQ has an instant response feature where you can send a queue and get an instant response, but Kafka only has one way to send queues. If that's something they could improve on, it would be great.

This is my second year working with this solution. 

I think it's very stable. I would rate the stability as a four or five out of five. 

The solution is very scalable. We started with a cluster of three and then scaled it to seven. I would give the solution a five out of five for scalability. Currently, we have 20+ employees on the technical team that are using the solution. 

We provide outsource services for other institutions. There is a whole set queue management form, and we have about five institutions, with three technical teams that use the same cluster.

There was a little learning curve, but we managed it. I think it took us around six weeks to complete the deployment. 

We have a team of three people who handled the deployment in-house. They also handle the maintenance for the solution. 

We do not use customer support, but there is a lot of documentation available.

I would definitely recommend this solution to other people. I would rate it as an eight out of ten.