Apache Kafka Reviews

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.

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.

We use Kafka for Elastic Stack and Kafka SCRAM login.

I have many users of Apache Kafka. It's like a subject to study in enterprises. However, we have not decided if the systems should generalize Apache Kafka for every application and every IT system.

We use Kafka for mapping and ThoughtSpot data from one IT system source to the destination. We also prefer it to exchange data from our internal IT systems.

Kafka is a new method we opted to apply to our need for data exchange. Also, we use the solution's integration capabilities.

Irovement-wise, I would like the solution to have more integration capabilities. Also, the solution's setup, which is currently complex, should be made easier.

I have experience with Apache Kafka.

It is a stable solution.

A lot of my experience indicates that Apache Kafka is scalable. We can have ten or even fifty hundred users on the solution. So, it's possible because we are a big enterprise.

I have experience with Apache Kafka's technical support.

The solution's initial setup process was complex. The deployment process took three or four years.

Right now, I can't deliver the planning process required for deployment.

For deployment and maintenance, we have a manager and an operational person. However, I can't give an exact count of the people required for deployment and maintenance.

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.

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.

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. 

We use Apache Kafka to process messages, specifically payment type messages, and incorporate the data from those messages into our analytics and reporting. It utilizes data from additional sources in real-time for our analytics and reporting purposes.

The real-time nature and the ability to use multiple offsets are the most beneficial features of Apache Kafka for our data streaming needs. This allows us to replay the same messages using different offsets. Although I haven't set up Kafka's scalability and fault tolerance myself, I know it can be configured with redundancy and fallback options. We primarily consume the messages using different clients, so the setup for fault tolerance and redundancy is transparent to us.

It's quite flexible and comparable to other solutions like ActiveMQ in terms of features and guarantees, especially with offsets for message handling. While ActiveMQ may be preferred in some use cases requiring guaranteed message delivery, Kafka's offset management provides similar functionality. Overall, I would recommend Kafka for real-time data streaming without hesitation.

The main challenge we faced while integrating Apache Kafka with other tools was setting up SSL and securing connections. Managing certificate changes and ensuring all clients connect smoothly, especially outside Kubernetes environments, posed ongoing challenges. Once initially set up, maintaining and sharing these security configurations became more manageable, but ensuring compatibility across different environments remained a continuous effort.

I have been using Apache Kafka for the last five years.

I would rate the scalability nine out of ten.

The technical support, typical for open-source solutions, is also responsive and helpful.

Positive

We switched to Kafka from paid solutions like IBM's MQ due to cost considerations, finding Kafka's multiple offsets and popularity advantageous.

As for pricing, Kafka is open-source, so it's free to install and use.

Lots of real-time processing and high-velocity data are the use cases.

I'm happy with the scalability and the ability to kind of replay the topics if you wish. So, it can give you that flexibility.

For the original Kafka, there is room for improvement in terms of latency spikes and resource consumption. It consumes a lot of memory.

Resource consumption. It consumes a lot of memory.

I have been using it since 2019. 

I would rate the stability a seven out of ten. There are issues due to latency spikes and resource consumption. It varies quite a bit. It's not very stable. It is a powerful tool; it can work, but it can be problematic sometimes. And that's why I switched to Redpanda.

I would rate the scalability a nine out of ten. One of our clients is an online casino; they have over two million end users. 

I used RabbitMQ. I switched to Kafka because it is just capable of handling a lot more messages.

And that was because the original Kafka had some performance issues, some latency spikes, and things like that.

The initial setup is easy because they provide documents. So, the documentation makes it easy to set up.

The deployment takes a few hours to set up a production environment and configure it in the cluster. It's pretty straightforward and pretty fast.

I figured it out on my own.

There is an ROI. 

If you use Confluent Cloud, it's expensive because it needs updates available in the platform, like AWS. But you only pay for what you use. So it's quite affordable considering the value it provides.

It is affordable for me. 

Overall, I would rate the solution an eight out of ten. I would advise integrating Kafka with Redpanda. It's easier to work with for most people.

I primarily use Kafka in the investment banking sector to update prices and inform clients of updates.

Kafka's most valuable feature is its user-friendliness.

There are some latency problems with Kafka.

I've been using Kafka for more than three years.

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