Apache Kafka Reviews

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.

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.

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.

Apache Kafka is used for more than only a messaging bus but also served as a database to store information. It functioned as a streamer, similar to ETL, to manipulate and transform events before migrating them to other systems for use. The database could also act as a cache. Apache Kafka is used as a database broker, streamer, and source of truth for multiple systems due to its ability to maintain events for at least 10 days. It provided both synchronous and asynchronous communication, making it a complex system that would be easier to understand through diagrams or sketches.

We use reactive frameworks.

From my experience with Apache Kafka, one of the most notable advantages is its ability to maintain a comprehensive record of historical data that includes every update, alteration, and version of information, unlike a conventional relational database. This feature allows for seamless tracking and analysis of the progression and transformation of the data over time, enabling users to easily review and analyze the history of the information.

The solution has the capability for various systems to effortlessly interact with one another without prior knowledge of their existence, current operational status, or specific configurations. By utilizing service buses and dynamic integration, data can be distributed across networks and retrieved in a way that is most suitable for each system's requirements. In addition, Apache Kafka allows for the modification of data to provide diverse clients, consumers, or observers with unique and varying data. The replication of data can produce multiple versions, and this data can be adjusted to fit various needs. With the use of probes, one can alter the behavior of the transformation process, thereby changing the way in which data is transformed and the output produced. Overall, working with Apache Kafka has brought about an array of benefits, enabling seamless system interactions and allowing for the customization and modification of data to meet individual requirements.

There are numerous possibilities that can be explored. While it may be challenging to fully comprehend the potential advantages, one key aspect is the ability to establish a proper sequence of events rather than simply dealing with a jumbled group of occurrences. These events possess their own timestamps, even if they were not initially provided with one, and are arranged in a chronological order that allows for a clear understanding of the progression of the events.

There have been some challenges with monitoring Apache Kafka, as there are currently only a few production-grade solutions available, which are all under enterprise license and therefore not easily accessible. The speaker has not had access to any of these solutions and has instead relied on tools, such as Dynatrace, which do not provide sufficient insight into the Apache Kafka system. While there are other tools available, they do not offer the same level of real-time data as enterprise solutions.

One additional area that I think could benefit from improvement is the deployment process on OpenShift. This particular deployment is quite challenging and requires the activation of certain security measures as well as integration with other systems. It's not a straightforward process and typically requires engineers who are highly skilled and have extensive experience with Apache Kafka to carry out these tasks. Therefore, I believe that there is a need for progress in this area, and some tools that can provide information, assistance, and help make the whole process easier would be greatly appreciated.

I have been using Apache Kafka for approximately four years.

The solution is stable if you have set it up correctly.

Apache Kafka is a scalable solution.

I have not escalated any questions to technical support because Apache Kafka is an open-source system. However, Confluent and other companies sell support and enterprise solutions to make it more convenient and streamline the work. They offer tools, such as a monitoring tool with a visual interface, which provides a lot of information and buttons to press for correction or change without touching the code. Each of those buttons hypothetically could have helped the situation, but it is unclear what they do exactly, it is best to call the data center and ask. If you buy their service, you have access to all the enterprise comforts.

Setting up Apache Kafka is, is not an easy task, especially when trying to containerize it and make it controllable. This is because Apache Kafka has its own distributed mechanism for staying alive, checking readiness, replicating, and scaling. Ensuring that it complies with Kubernetes or OpenShift Orchestrator requires careful attention, as there is a risk of two masters attempting to perform the same task and ultimately undoing each other's work.

In comparison to Kubernetes, OpenShift is a highly skilled and advanced implementation infrastructure that automatically manages and orchestrates all the steps required for an application setup. It operates at a higher level of abstraction and eliminates the need for manual operations that are required with Kubernetes. While Kubernetes can run an application with some pipeline and configuration, OpenShift takes care of everything from finding the required images to creating ports and connecting databases. Although manual changes can be made, it's not necessary as OpenShift offers a much more course-grained management approach.

One skillful DevOps engineer can implement the solution.

Apache Kafka is an open-source solution.

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.