What advice do you have for others considering Apache Kafka?

When implementing Kafka, it's important to plan the cluster size upfront to ensure easy scalability. Adding or removing nodes can disrupt the clusters, so proper sizing and planning are key. I would rate Kafka as a solution as a nine.

I definitely recommend Kafka, as it is the industry standard for streaming platforms. While Red Panda is similar, Kafka remains the stronger choice in the market for its established support and usage in big companies. I'd rate the solution nine out of ten.

Kafka is one of the most convenient tools with a fair level of performance. Its stability is impressive, allowing us to recover from data center blackouts quickly. I highly recommend it for similar needs. I'd rate the solution nine out of ten.

I did not come across any scenarios involving fault tolerance because when it comes to the issue data consistency issues, like missing or incorrect value of data are actually part of the system where the data is being fed. Nevertheless here, when it comes to the missing values, I never tried the option, especially whenever a value is missing, that can allow one to impute the value with another parameter. Speaking about if I incorporated any emerging data stream streaming trends in Apache Kafka workflows, for example, utilization of AI, I would say that I use it as a local system, so if I have an EC2 server where I kind of read the sample and then populate the regression and reintegration model on top of it, but that is done locally and not on the cloud. I recommend the product to those who plan to use it. I like Kafka and Flink, and I want to actually create a system in AWS mainly for real-time streaming so that I don't need to worry about multiple data copies. Considering the improvements needed in the product's support, and the cloud integration capabilities, while looking at the simplicity during the installation phase, I rate the tool a seven out of ten.

I rate Apache Kafka a nine out of ten for its performance, features, and community support.

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.

Apache Kafka as a broker tool is a very stable and good product. When you need to create a consumer in any programming language, including Java, Golang or any other programming language, the team involved in the process of the creation of a consumer should have very strong knowledge and expertise in the use of Apache Kafka since it is not at all easy to create a consumer for the product. A highly qualified person with a good amount of experience should also know the internals of the solution, which may not seem too straightforward. Anyone cannot use Apache Kafka easily without proper knowledge or experience. When you use Apache Kafka in your actual application, you need to create some producers and some consumers. To create a consumer, you need to have a very strong understanding of the solution since it is not a process that anyone can manage easily. A company needs to have a very strong team with good technical knowledge to be able to use the product. I rate the overall solution a nine out of ten.

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.

To be able to recommend Kafka to others, especially considering every context, we will have to set a benchmark and compare Kafka with other tools. I rate the overall solution a seven out of ten.

From an architecture and solution design perspective, I would say that before going for streaming solutions, we should analyze the data, which might be old, and decide if it's a streaming use case or not. Often, people think it's a streaming use case, but when they perform analytics on top of it, they realize they can't do a month-to-date or year-to-date analysis. So, it's essential to think again from the data basics perspective before going to Kafka. Overall, from the product and solution perspective, I would rate it a nine based on my personal use of data.

I would give Kafka a rating of seven out of ten.

The maintenance of Apache Kafka is crucial due to the complexity of the system with numerous microservices and systems communicating through Apache Kafka, requiring proper integration and configuration to prevent overloading and ensure a healthy cluster. The task is not easy and requires knowledge of the various adjustable parameters, as misadjusting even one of them can greatly slow down the cluster. For example, if the consumer group changes frequently, the messages must be regrouped and reassigned, causing significant delays. Therefore, configuring Apache Kafka correctly is essential to avoid high latency issues. I would strongly suggest others give Apache Kafka a chance and explore the various advantages that it can offer, especially since it should not be perceived as a message bus or broker but rather an enterprise bus designed for data manipulation. It has the ability to transform data, store and reject it, and even maintain different versions of the same data simultaneously. Moreover, it operates on a pull mechanism rather than a push mechanism, which takes away the risk of losing data and places the responsibility for data loss on the consumer. On the other hand, it also ensures that the data is always available within the specified window and allows for easy replication of the past, which is extremely helpful in situations such as those involving a hacked bank database. With Apache Kafka, you can efficiently go back in time, obtain the required status and events, and make changes accordingly, without the need to go through each transaction separately. Thus, using this solution can make data management much more efficient and convenient. I rate Apache Kafka an eight out of ten. In order to improve its user-friendliness, engineer-friendliness, and DevOps-friendliness, the system must undertake various tasks, such as enhancing the overall operation and configuration, ensuring seamless integration with other systems, and adapting to security layers in a more comprehensive and generic manner. This will require significant efforts to make the system more functional, secure, and efficient.

I don't see any major issues with using Apache Kafka. Many companies use it and it's a good solution. My advice would be to use it as a software-as-a-service rather than setting up your own cluster. This way, you can benefit from a preconfigured and maintained platform. It's better to opt for a software-as-a-service solution. I rate Apache Kafka an eight out of ten.

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

Since it has become so popular, large enterprises especially want to do it. For smaller enterprises, Kafka would probably be too expensive because they would have to hire people to maintain it. I would rate the Apache Kafka solution a seven out of ten.

I give the solution an eight out of ten. We test all the supported versions of the solution based on our customers' use. We support our integration product. So we need to do dev and QA with Apache Kafka or any other messaging applications. But we do not provide support. The solution can be supported by someone else. We don't need to have any specific staff for deployment. All the developers in QA can install and configure the solution. We don't have a separate person for maintenance. Our team and our product dev and QAs all use the solution. I think Apache Kafka is a good solution and I recommend it to others.

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.

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.

The documentation can be a challenge. There are quite advanced capabilities of Kafka, like the transformations that you can build to modify the data as needed. We found that the biggest challenge was documentation and being able to gain the knowledge of exactly how to do stuff. We also struggled on the transformation, but other components were fine, so some parts are good, and some parts are bad. I would rate this solution as an eight out of ten.

We had a good experience with the solutions, the maintainability and scalability are good. I would recommend the solution to others. I rate Apache Kafka a nine out of ten.

I rate this solution a nine out of ten for streaming. I recommend it to other people. The solution is good, but its performance can be improved.

I would recommend that other businesses do the deployment themselves, but manage the tool with the aid of a service provider, rather than in-house. I would rate this product seven out of ten.

There is room for improvement with this solution so I rate it eight out of 10.

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

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.

I would rate this solution 7 out of 10. I would recommend this solution because the queue manager is very fast and stable.

I would rate this solution 8 out of 10.

I rate Apache Kafka nine out of 10. I think it's one of the best tools on the internet.

I would recommend trying this solution, but you should probably run it on Linux. I like this product, I would rate Apache Kafka a nine out of ten.

I recommend this solution, we're probably going to use it again in another project. I rate this solution eight out of 10.

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.

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.

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.

I rate this solution an eight out of 10.

This is a solution that I may recommend, but its suitability depends on the needs and requirements. I would rate this solution an eight out of ten.

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

I think that many people are using Apache Kafka just as a publishing and subscription model, but I feel that Kafka is better than that. Furthermore, Confluent Kafka is even more than that. Confluent Kafka is offering features that are equal to those of a data lake. You can do lots with data, and huge data can be persisted. However, many people are not using that feature. Rather than make use of persistence logic, they are pushing the messages and consuming them. Maybe if people were using it for persistence, they would see the impact or real power of Kafka. I would rate this solution a seven out of ten.

I would recommend trying this solution. Take the time to understand it because it is a different style when it comes to working with data. I would rate this solution a nine out of ten.

What happens in our company is a little different. We basically provide services to other companies through Kafka, like our management services. It doesn't necessarily mean we're using the solution ourselves, however, we will be going and deploying Kafka for companies, like a systems integrator. The version of the solution is normally 2.4, however, it depends on the requirements. Our cloud providers are always different due to the fact that the countries that we work with are all different. For example, in the US it could Amazon, Azure, or Google. It varies. I'd advise other organizations considering using the solution to make sure they understand what the use case is. They need to know what their services will be and if they will be directed to Apache Kafka. From a customer perspective, potential companies need to make sure they have an idea of how big it's going to be due to the fact that it's a cluster environment. It needs to be taken care of. Customers will need to know things like what is the message rate is which is coming into Kafka and how they will connect all those different microservices or any services together to Kafka. From an infrastructure perspective, it's more of how big of a cluster a company needs. Who would be the producers to produce it, and who's the consumer who's consuming the data are a few questions that need to be asked. I'd rate the solution eight out of ten.

Apache Kafka is a good solution with many good features but for large deployments, I would choose IBM MQ over Kafka. I would rate this solution a seven out of ten.

My advice would be to go through the documents and understand the topics. Learn what its effects are and take care of partitioning. Based on my experience, I would rate it an eight out of ten. It's quite complicated and the configuration requires a lot of effort. As a developer it is quite hard to go into all these things.

Although we are deployed on-premises at the moment, we are looking to have a cloud-based deployment in a year or two. This is a solution that I can recommend but it will take a lot of time to develop the adapters. I would rate this solution a seven out of ten.

This is currently the product that I am recommending to customers. Some customers want an open-source solution. There are some newer products that are coming on to the market that are even faster than Kafka but this solution is very resilient. In the long run, I think that open-source will dominate the pace. I would rate this solution a seven out of ten.

I'd rate the solution eight out of ten. It's good at scaling, and, performance-wise, it's excellent. If they could add upon the UI and allow for easier configuration, I'd rate them higher.

I would rate it a nine out of ten. Not a ten because of the monitoring and admin improvement I'd like for them to make.

We're using the 2.1.30 version of the solution for our cloud-based clusters. We use the on-premises deployment model. Most customers use the on-premise solution for cloud-based clusters. Kafka is a very good solution for log management. If you need anything done related to log management, Kafka can do it. Kafka can also store the data in the brokers. This prevents data loss as well as the duplication of data. It's quite comprehensive. I'd rate the solution seven out of ten. If the solution could provide a user interface I'd rate it higher. This is important for managing Kafka's clusters on the administration side. It would also be helpful if two to three files could be minimized to one configuration file.

In this type of solution, you need to be able to accept a high volume of messages, but not lose any, and not have any duplicates. Because we are unable to control the queue in Kafka, I cannot say that this works 100%. The suitability of this solution depends on the use cases. There are two or three things that we are worried about, and we will be very careful in choosing solutions. In cases where the messages are well organized, or there is no worry that there will be duplicate or dropped messages, then I recommend using Kafka. Also, I recommend this solution for those looking to get involved with open-source applications. Other than the problems with having no control over the queue, Apache Kafka is wonderful. I would rate this solution an eight out of ten.

I would definitely recommend Kafka. In our current position, we use it to move a lot of data and I think it's definitely working well. I would definitely recommend it. I would rate it an eight out of ten.