Apache Kafka Reviews

We are using Apache Kafka to extract data from a Portuguese data source, utilizing an open-source project for data capture. The connector for this project is linked to both Kafka and Confluence platforms. We then transform the extracted data and store it in Elasticsearch.

We appreciate the ability to persistently and quickly write data, as well as the flexibility to customize it for multiple customers. Additionally, we like the ability to retain data within Apache Kafka and use features, such as time travel to access past customer data. The connection with other systems, such as Apache Kafka and IBM DB2. 

Apache Kafka can improve by adding a feature out of the box which allows it to deliver only one message.

I have used Apache Kafka within the last 12 months.

Apache Kafka is a stable solution.

The scalability of Apache Kafka is good. It can process many requests simultaneously.

We have approximately 600 people using this solution in my organization.

I have not contacted the support from Apache Kafka.

The initial setup is relatively easy as I am using Docker and the files provided by Confluent. However, setting up Apache Kafka in a production environment is not as straightforward. I prefer to use solutions, such as Confluence that already have everything preconfigured. As a developer, creating an environment for it is not a problem for me, but I think it can be challenging for those responsible for the production environment. There have been issues with data loss and other problems in the past. Configuring it for production is not easy.

My deployment was very quick because I am using it locally. We have someone else that does the cloud deployment.

I did our local implementation and we have someone else that does the cloud deployment.

The price of Apache Kafka is good.

I rate the price of Apache Kafka an eight out of ten.

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.

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.

We have a scalable architecture where we need multiple workers to handle some processing. To make it possible, the backend catches the request and puts it in a common medium, which is the queue of Apache Kafka. The workers then can share and process it.

The most valuable feature of Apache Kafka is the clustering which is very easy to scale and we have multiple servers all over our platforms. It has been useful for stability and performance.

Apache Kafka can improve by providing a UI for monitoring. There are third-party tools that can do it, but it would be nice if it was already embedded within Apache Kafka.

I have been using Apache Kafka for approximately two years.

Apache Kafka is stable. We have not had any issues.

the scalability of Apache Kafka is good. We have parts of the information we use in different geographical sites and it doesn't pose any problem.

I have not used technical support.

I previously used RabbitMQ. We switched because Apache Kafka was more stable and had better performance.

The initial setup of Apache Kafka was easy because it is Dockerized. However, if you were to install it yourself it would be difficult. Having it Dockerized makes it worth it. 

The first deployment took approximately two hours. The updates of the solution can be done in a matter of minutes.

Our DevOps team in our IT department did the deployment of the solution. It was mostly virtual work. The maintenance of the solution does not take a lot of time.

We are using the free version of Apache Kafka.

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 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.

We use Apache Kafka for financial purposes. Every time one of our subscribed customers is due for an insurance payment, Apache Kafka sends an automated notification to the customer to let them know that their bill is due.

Resiliency is great and also the fact that it handles different data formats. There is one data format that's universal across multiple application domains — Avro. It's pretty universal compared to JSON, XML, SQI, and other formats.

Some vendors don't offer extra features for monitoring. Some come with Linux for default monitoring. Monitoring is very important. If something is not working properly, then our subscribers won't receive a notification. You then have to trace it back to Kafka and find the glitch or the messaging sequence that hasn't been racked up correctly.

It should support Avro — which handles different data formats — as a default data format. It would be much more flexible if it did.

I have been using Apache Kafka for three years.

It seems to be quite stable.

Apache Kafka is Scalable. You can actually launch a server node or a broker. Three nodes and Zookeeper (the Kafka server management system) is optimal. If one of them goes down you can automatically launch another one. You can go three servers or brokers back — there's a repetition on each Kafka broker.

Apache Kafka is open-source. They don't offer technical support.

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

We use the product for high-scale distributed messaging. The processing capability of the product is enormous. Being a distributed platform, multiple consumers can sync with it and fetch messages.

Another great feature is the consumer offset log which tells you where the consumer left and where he needs to start again. Consumers aren’t required to code and put extra effort to maintain the offset.

We were using another commercial messaging engine, which was not scalable unless you paid more. Each hub that we provisioned was expensive. This solution is open source, which is much easier to use and doesn’t cost us anything.

This product guarantees at-least-once delivery. We have asked JIRA to provide features such as at-most-once delivery to remove duplicate message consumption.

We haven’t faced any issues so far. Some of the clusters churn millions of records per seconds with ease.

We have clustered environments and we haven’t seen any scalability issues. We can provision a new node in as little as 45 minutes.

It is open source, so support is in our own hands. The only option is to make a new feature request through JIRA. When multiple people in the community make a request for similar feature, it gets priority.

We switched from a previous solution mainly to reduce costs and to have a more scalable solution.

The initial setup was a bit complex in terms of how to manage it across data centers. But once it was setup, we never faced issues.

We evaluated multiple options, such as ActiveMQ and RabbitMQ. We leaned towards this solution.

I would advise others to start with non-SSL implementations and try to do PoCs. Afterwards, they should move towards more secure features.

We are always using Apache Kafka for our real-time scenarios. It helps us detect anomalies and attacks on our website through machine learning models.

We are managing our data by topics. Splitting topics is more effective for us. Apache Kafka is very fast and stable. It offers scalability with ease and also integrates well with our tools. Fault tolerance is a good feature, and it also has high throughput rates.

Config management can be better. We are always trying to find the best configs, which is a challenge.

I have been working with Apache Kafka for more than four years. It has been used since the beginning of our department, maybe six years.

It is very stable and meets our needs consistently.

If there is latency, our Kubernetes admin includes our Kafka nodes to increase scalability. Kafka provides flexibility and integrates easily with Kubernetes.

Before Apache Airflow, I used Cron Tab. However, Apache Airflow makes it easy to follow and manage tasks, and data science departments can easily build their models or pipelines using it.

I would rate Apache Kafka nine 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.