Apache Kafka Reviews

Apache Kafka can be deployed on the cloud and on-premise.

We use Apache Kafka internally to build a service on a cluster. Additionally, we use the intermediate persistence layer for events. There are many teams who leverage it as a message queue and further their microservice connections.

Apache Kafka has helped out the organization because we leverage it for all our eCommerce real-time analytics use cases.

The most valuable feature of Apache Kafka is Kafka Connect.

Apache Kafka could improve data loss and compatibility with Spark.

I have been using Apache Kafka for approximately five years.

Apache Kafka is stable.

The scalability of Apache Kafka could improve.

We have approximately 10,000 users using this solution.

The support from Apache Kafka could improve. Their engineers at times do not know what the solutions can do.

We previously used IBM MQ, Tipco, and AMQ.

The initial setup of Apache Kafka was complex. We were able to simplify it by doing registry-based integration of the services.

Apache Kafka has given a substantial return on investment.

The number of people required for maintenance depends on the team. They need a centralized team to offer Apache Kafka and services. Each team does have knowledge of Kafka.

This solution has a lot of features and there is no other solution on the market that has similar advanced features. It is a very good solution.

I rate Apache Kafka an eight out of ten.

I worked with Apache Kafka for customers in the financial industry and OTT platforms. They use Kafka particularly for data streaming. Companies offering movie and entertainment as a service, similar to Netflix, use Kafka.

Apache Kafka offers unique data streaming. It allows the use of data in motion, allowing data to propagate from one source to another while it is in motion. This is valuable when data is not simply residing in a database.

In the data sharing space, the performance of Apache Kafka could be improved. The performance angle is critical, and while it works in milliseconds, the goal is to move towards microseconds.

I started working with Kafka about five years ago while at a financial company.

Apache Kafka is stable. Even though enterprises often use the open-source version, there are minimal issues after configuration.

Apache Kafka is very scalable. I would rate its scalability as nine out of ten. Customers have not faced issues with user growth or data streaming needs.

The Apache community provides support for the open-source version. Despite being open-source, extensive documentation is available to resolve issues.

Positive

The initial setup of Apache Kafka is straightforward, around an eight on a scale from one to ten. The deployment process involves configuring the publisher, subscriber, and other parameters. SaaS can be deployed from the cloud in a couple of hours.

Since I work with the open-source version of Kafka, solutions are managed internally with the Apache documentation.

The open-source version of Apache Kafka results in minimal costs, mainly linked to accessing documentation and limited support. Enterprises usually opt for the more cost-effective open-source edition.

For critical business components, it is advisable to use Confluent-managed services for Kafka. However, for non-critical functions, the open-source version is sufficient. 

Overall, I rate Apache Kafka as nine out of ten for its scalability and stability.

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.

Apache Kafka is a messaging solution where you have topics to pass on your information. You can send messages to multiple topics.

We need to manage limited resources. Additionally, we can send or push messages through a specified port. This is a significant feature because, unlike traditional queues, Kafka uses a cluster of nodes, making it easy to integrate with various algorithms. This clustering is an advantage and a key feature of Kafka, providing good interaction and scalability.

For example, when you want to send a message to inform all your clients about a new feature, you can publish that message to a single topic in Apache Kafka. This allows all clients subscribed to that topic to receive the message. On the other hand, if you need to send billing information to a specific customer, you can publish that message on a topic dedicated to that customer. This message can then be sent as an SMS to the customer, allowing them to view it on their mobile device.

Apache Kafka is different in its design. If you have topics around the front end of clusters in the facility, it is scalable. The software is scalable to handle and process data. However, it might not be suitable for handling specific types of images or media files. Other than that, it should handle the rest of the data processing needs.

There are no multiple versions, which simplifies the process of granting access with Kaspersky. Every message is accurately delivered. However, Kafka does not support sending messages directly. You need to publish messages finalization. If you want to resend a message, you must resend it manually. Kafka does not automatically handle this. Another thing is the need for a redo option if an issue occurs. If a message is not sent properly, it can be retransmitted within the core system. You should enable the gateway in your program for it to function correctly. Messages will not be delivered or refreshed unless you enable the direct replay option in the product settings.

I have been using Apache Kafka since 2020-21

The initial setup of Apache Kafka is challenging and requires experience. Each message should always receive a response, so prioritizing traffic is essential. Furthermore, the client or consumer must always be in sync, or the message will not be processed.

One pair of nodes is sufficient for the system. If our other system requires more than five nodes, it might not be feasible. Currently, other components are functioning as expected. The Kafka setup won't take much time.

When using Apache Kafka, it’s important to manage different environments carefully to avoid confusion. For instance, you can configure different client applications for producing and consuming messages. Ensure that the configurations for each environment (development, testing, production, etc.) are separated. This includes managing source code and data appropriately to maintain security and efficiency. Proper management of Kafka assets and operations phases is crucial for a smooth workflow.

I recommend Apache Kafka since it is extremely fast, stable and has been used for a very long time. We haven't encountered any major issues or concerns regarding its performance and customer service.

Overall, I rate the solution a nine out of ten.

Our primary use cases allow software developers, and application developers, the option to not have to code in their own logic for the retry mechanism. A lot of software, and applications, have this feature of retry built in some way or the other, but they all have some kind of a pre-alpha version of Kafka, up to a certain extent.

So it is a good backbone for microservices. So basically you want to write microservices, which you can shut down and bring it up whenever you want. You want to be able to shut it down to actually replace it with a newer version and bring it up. The bottom line is you can kill the microservice and bring it back up and do all the things that you want to do with it. But whenever it comes back up, it should pick up and run from where it had left off. That is what everybody tries to do. And in order to build such a system, they have to write several logical pieces of code, and most of that code has already been built for in Kafka so that you don't have to do it yourself.

One of the most valuable features I have found is Kafka Connect.

Basically, the bootup time, if you have large messages, sometimes takes up more time than I would really like it to. So that is the area that Kafka can actually improve upon. But that is okay, the way we get around it is to make sure that Kafka has started up first and warmed up before anything else starts up. I would also like to see monitoring service tools.

I have been using Apache Kafka for the past three years.

The stability is good as long as you have a short retention period.

Confluent is the cloud version of Apache Kafka and it is scalable.

We do the implementation in-house.

If you are managing your own implementation the return on investment is pretty good. What you need is good developers.

Apache Kafka has open-source pricing.

Apache Kafka is a good choice, so I would recommend people not have a real-time application if they do not have to. It is better to have a very fast batch operation than a real-time operation. I would rate Apache Kafka a nine on a scale of one to ten.

Apache Kafka is an open-source solution that can be used for messaging or event processing.

Apache Kafka has performance issues that cause it to lag.

We did a couple of POCs on Apache Kafka for more than two years for messaging and event processing.

I rate Apache Kafka an eight out of ten for stability.

I rate Apache Kafka a seven out of ten for scalability.

Since it's an open-source solution, there is no technical support, and users often rely on the community edition.

I have previously worked with Confluent and Anypoint MQ. Confluent is completely an event-driven architecture. Anypoint MQ is a typical messaging software and cannot be used for an event-driven architecture.

The solution's initial setup is quite straightforward. You just have to upgrade a couple of configuration files.

Apache Kafka is an open-source solution.

A non-enterprise business with a low message load can use an open-source solution like Apache Kafka.

I would recommend the solution to enterprise businesses depending on their use cases. Suppose an enterprise business doesn't have any integration or a middleware platform and wants to do a greenfield implementation. I'll evaluate the use cases and refer Apache Kafka to them if messaging is needed only for exception handling or transferring the messages.

I have recommended Apache Kafka to some customers who wanted asynchronous messaging for logging purposes. Those messages were not business-critical messages as such.

I would recommend Apache Kafka to other users. Apache Kafka is more relevant when we use open-source integrations and when customers want to reduce the TCO. As an architect, I recommend the solution to customers based on their messaging needs. Apache Kafka and Anypoint MQ are the only two messaging products available today. The open-source Apache Kafka is always recommended if the customer really doesn't want to get into any of the license models.

Overall, I rate Apache Kafka an eight out of ten.

We use Kafka daily for our messaging queue to reduce costs because we have a lot of consumers, producers, and repeat messages. Our company has only one system built on Apache Kafka because it's based on microservices, so all of the applications can communicate using it.

Kafka is an open-source tool that's easy to use in our country, and the command line interface is powerful. 

The user interface is one weakness. Sometimes, our data isn't as accessible as we'd like. It takes a lot of work to retrieve the data and the index.

I've used Kafka for about 10 months.

Kafka is stable.

We can't access support because we are in Iran, and many countries prohibit business with Iran. 

We used MSMQ on Windows, but we decided to migrate our system to Docker and we wanted to use base Linux, so we move them from Amazon Queue to Kafka.

Apache Kafka has one advantage that sets it apart from other providers. We need to iterate on the messages, but others don't have this feature. Kafka has partitioning, which is useful, so we decided to go with Kafka. 

I rate Kafka 10 out of 10 for ease of setup. It's easy for us because we use Docker, but if you want to use another system like Linux it may be a little challenging

Kafka is free. 

Redis has an open-source solution, but I'm not sure about IBM. I haven't researched it. 

I rate Apache Kafka seven out of 10. It's a good solution. They're constantly fixing bugs and adding new features. 

I have previous professional experience using Kafka to implement a system related to gathering software events in one centralized location. 

One example of how Kafka has been beneficial is in the context of financial trading. When a trade is executed, it generates an event. I used Kafka to create an application that captures these events and stores them in a topic, allowing for efficient processing in real time.

Regarding the most valuable feature in Kafka, I would say it's scalability. The publisher-subscriber pattern and low latency are also essential features that greatly piqued my interest.

Maintaining and configuring Apache Kafka can be challenging, especially when you want to fine-tune its behavior. It involves configuring traffic partitioning, understanding retention times, and dealing with various variables. Monitoring and optimizing its behavior can also be difficult.

Perhaps a more straightforward approach could be using messaging queues instead of the publish-subscribe pattern. Some solutions may not require the complex features of Apache Kafka, and a messaging queue with Kafka's capabilities might provide a more complete messaging solution for events and messages.

I have been using Apache Kafka for the past 10 years. 

The stability may improve if the configuration and management aspects become less challenging.

It depends on the configuration., but scalability is one of the best features of Kafka. I would rate it nine out of ten.

Support can vary depending on whether you're using the open source version or a paid one. Our version, the paid console version, offers highly available support, and you can find a wealth of information and assistance from various providers online. However, when I used MSA on AWS, I encountered limited support for it.

Neutral

Despite the challenges we faced with configuration and management, I believe the return on investment is safeguarded.

The cost can vary depending on the provider and the specific flavor or version you use. I'm not very knowledgeable about the pricing details.

I believe that when working with Kafka Apache, it's essential to have a specialist who thoroughly understands and can optimize all the available variables within the solution to achieve the desired behavior.

I would rate it an eight out of ten.