Apache Kafka Reviews

The primary use case of the solution is for asset communication through our microservices.

The solution has allowed us to take the use cases provided by another communication tool and resolve those issues.

The most valuable feature is how persistent it is. For example, we are able to reprocess messages when we need to, we're able to recover methods to consume them.

The solution can be improved by reducing the cost to run it on the premises.

I have been using the solution for four years.

The stability of the solution is good.

The solution is scalable.

The initial setup was straightforward.

The implementation was through a vendor.

I have seen a return on investment with this solution.

I give the solution a nine out of ten.

We have 80 people using the solution and five people are required to maintain it.

I suggest using cloud services because the solution is expensive if you are using it on-premises.

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 have a camera monitoring security system, in which we post messages onto the queue, which involves various steps in processing the message, like checking for the number of clients, running it against the police data, etc. So Apache Kafka is a security application with many types of consumers. We set up a workflow system with different sites, which works well.

As a software developer, I have found Apache Kafka's support to be the most valuable. The support team sends available information regarding the library and how to use the plugins. The solution is easy to integrate with any of our systems. We have other alternatives, but this is the one that seems to be the most popular database support.

The solution should be easier to manage. It needs to improve its visualization feature in the next release.

I have been using it for three years.

It is a stable solution. We never faced any issues. I rate it a ten out of ten.

It is a scalable solution. We set up a category with different consumers balancing things, which works as I thought.

I did not contact the technical support as it was not required.

We used Linksys for visualization along with Confluence, but there needed to be more value. For us, Apache Kafka is the best solution based on the support and third-party systems as it builds our subsystems around because we have a lot of development teams.

The initial setup was straightforward because I've got a lot of experience in this field. But even for a junior person, it would be fine. There are so many resources, and it's very well documented as they are a premium service provider. So it makes the setup just easier.

The deployment takes a few days.

We set up a free cluster for this service because we use a lot of data. We use ZooKeeper to secure different products for instruction with the cluster. But, it was easy as it is a popular product, and much information is available. It can download data, like fifty gigs per day. We can effectively handle it all as well. I never developed any issues.

It's a premium product, so it is not price-effective for us.

Apache Kafka is an out-of-the-box, reliable solution. For people in the fiber business, we need a reliable solution, and this solution is hundred percent reliable. If it is set up correctly, it hardly has any issues due to the more extensive user base; even if there are issues, it is sorted by the community. I rate it nine out of ten.

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.

I use Kafka to send network packets from different sources to my cluster. We have around 10 users at my company.

Kafka is scalable. It can manage a high volume of data from many sources.

The interface has room for improvement, and there is a steep learning curve for Hadoop integration. It was a struggle learning to send from Hadoop to Kafka. In future releases, I'd like to see improvements in ETL functionality and Hadoop integration. 

I have used Kafka for around six months.

I rate Apache Kafka seven out of 10 for stability. 

I rate Kafka eight out of 10 for scalability. 

I rate Apache support six out of 10. It was hard to find the information I needed. 

Neutral

Before Kafka, I sent feeds directly to Hadoop.

I initially found Kafka difficult to set up, so I would rate it about five out of 10 for ease of setup. After I learned more about the platform, I would rate it eight out of 10. It is deployed on-premises over a cluster of three or four PCs. You can deploy Kafka in a few hours with one person. 

Kafka is open source. 

I rate Apache Kafka eight out of 10. I would recommend it to others.