Apache Kafka Reviews

We use it extensively in our data pushing, for analytics and all of this type of data that is pushed, rather than on a real-time and payment basis. However, we are using it for offline messages, pushing it for processing, and for heavy, heavy usage, rather than extensively using it for financial data.

The main advantage is increased reliability, particularly with regard to data and the speed with which messages are published to the other side. 

The connectivity from the application is straightforward, as is the API integration.

These are some of the most valuable features of this solution. 

In terms of partition tolerance, message reliability is also present, which is a very good feature from the customer's perspective.

The area for improvement in Kafka is difficult to say because it's a solid product that works well in its intended applications. And, we are looking for something that can be used as part of financial implementations, because we don't want too many messages to be delivered to the other side, which is one of the areas I am looking at as well.

One of the things I am mostly looking for is that once the message is picked up from Kafka, it should not be visible or able to be consumed by other applications, or something along those lines. That feature is not present, but it is not a limitation or anything of the sort; rather, it is a desirable feature.

The next release should include a feature that prevents messages from being consumed by other applications once they are picked up by Kafka.

Then there is message dependability because a message is of no use if cannot be consumed. Alternatively, if the message is consumed but not committed, it should not be recorded in the Kafka queues. It should be because that is one of the features that is existing in MQs consistently provide: if the message is not committed, it will be committed back to the queues.

I have not seen that in Kafka.

We have been using Apache Kafka for approximately three years in the organization.

I believe we are working with version 10. Confluent Kafka is what we are using.

It's a stable solution. Once completed, it is a very stable solution.

The scalability is very good. It is scalable horizontally rather than vertically. 

It can scale up to any level horizontally. However, if the message, once used horizontally scalable, cannot be shrunk once the requirement is reduced, some process is actually taking place. That is one thing that is lacking.

I believe there are approximately 10 to 15 people who use it.

This is being used by the data migration, data team, data analytical team, and data engineer. It's being used by all application architects who are just looking into it, as well as middleware integrators and middleware application integrators.

We have big plans to increase the use of various other innovations and stuff like that. We are using it in relation to data activities. 

Also, we are only planning to use the financial part for publishing it, subscribing, and publishing a pop-up model for various use cases.

Apache usually has a community deployment. If you use Apache or any other software, you will usually receive community support. Otherwise, some companies are taking it and beginning to process it. For example, in Kafka, there is a version of Confluent that they use and support. Or, as we call it, the Oracle Big Data platform.

It will be included with Hadoop, Spark, and other similar technologies. That is coming as, one of the back software packages that are part of that offering, and it is supported by Oracle. Depending on the type of open source, there are various types of support available. Other than the community, we will not receive assistance. Otherwise, it's free enterprise, and we can take it from Confluent or other vendors who offer similar products.

Prior to implementing this solution, we were not using another solution. We have been using, Kafka from the beginning with regard to these use cases. However, we are using other queuing solutions, such as MQ, ActiveMQ, IBM IQ, and Q, but the use cases are different. This is primarily due to the large volume, faster processing, and other benefits of using Kafka.

It is not deployed on-premises. 

We use Kafka as part of the OCI Oracle Cloud platform and the Oracle Big Data platform because Kafka is included.

The Apache Kafka setup will take some time because it is not simple, and we have a lot of other components to install. It's fine because we needed all the plugins and other things for the simple implementations, but the containers' implementation is simple. The only difference is that when it comes to Zookeeper, there are a lot of supporting applications running on top of it, such as Zookeeper. As part of their area, Apache Kafka is running on top of Zookeeper. What do they think? As part of their... manageability, the Kafka area, and Apache Zookeeper. As a result, everything must be removed. And it will be preferable if the implementation is simple.  I believe Confluent is doing this, but we have not yet begun.

The deployment, and configuration, will take one hour to complete. However, it is also dependent on the fact that you require a large number of configurations, which we have.

The deployment was completed in-house.

Currently, there is a team of three to maintain this solution. There are application support personnel in charge of access control.

It will be included in the Oracle-specific platform. It is approximately $600,000 USD.

When it comes to Apache Kafka, they must understand how it works and what its internals are. There could be numerous challenges associated with the product and its entire life cycle. You will have to have a good understanding and knowledge of the configuration. You will need a technical person who is knowledgeable in Kafka which will be an advantage and on an ongoing life partner.

It's a very good solution, I would rate Apache Kafka a nine out of ten.

We use Apache Kafka for patching real-time data that we receive over a data transport layer and for putting the data into Apache Kafka. From Apache Kafka, we use several applications to subscribe to topics from different applications that we serve directly to browsers. Additionally, we use these applications inside our solution and have Apache Kafka Stream, which is connected to MongoDB.

Since we receive data in real-time consisting of IoT devices, running vehicles, their locations, their states, and their VNs, the solution is helpful.

The product could be improved with proper documentation. Proper documentation should be the SSE. We have a challenge with configuration, so it isn't easy to configure a standalone Apache Kafka on the premises. It needs to be set up on-premises and surveys being provided in the market want to be excluded. Hence, being a developer and configuring Apache Kafka is very hard. It is user-friendly, but initially, we found it challenging. Improving the documentation in this solution would be much better if documents were provided on GitHub for different things. As the market is growing, Spring solution is working hard to get products in the market so when Python, React JS, and Node.Js came, they were lacking. But today, Spring Boot has a solid framework. So the support on Apache Kafka could be improved, but finding some configurations with Spring Boot isn't easy.

We have been using this solution for over three years and are currently using the latest version.

The solution is stable, and the most fantastic thing about it is its throughput. For example, I have tried MQs, which also have Apache Kafka Streams. So the throughput of Apache Kafka Stream is impeccable and impressive.

The solution is very scalable, and its availability is brilliant. We have approximately 32,000 people on our customer base.

We do not have any experience with customer service and support.

We have tried different MQs, but the subscription and charting available on this solution are better. We have used Queues previously, but this solution is more stable, so we chose it.

The initial setup is dependent on the individual. For example, it would be straightforward if a person practices these things a lot and understands the documentation correctly. However, since most people prefer examples instead of reviewing documentation, it would be easy to set up if they find steps on the internet but difficult if they do not have examples.

I rate the pricing for this solution an eight out of ten. It could be a bit cheaper.

I rate this solution an eight out of ten. It is good, but the documentation could be improved.

We used Kafka as a central message bus, transporting data from SNMP through to a database. Some of the processing in between was handled by other components.

We built a solution for a client and the client was happy with the solution.

The message bus capabilities, basically sending messages to it, and the way it handles events or messages is pretty good. The throughput was good. Generally, it was a good component.

We struggled a bit with the built-in data transformations because it was a challenge to get them up and running the way we wanted. There was a bit of a learning curve. It may be that we didn't fully grasp the information.

Also, the documentation covering certain aspects was a bit poor. We had to trawl around different locations to try to find what we needed. When we were able to find documentation on transformation, for example, there wasn't a good set of documentation examples we could use, and the examples we had weren't quite meeting the need. Better examples would've helped us.

I used this solution for about a year and a half. 

It seemed pretty stable and didn't have any issues at all.

I don't know how many people were using it on the client's side, but we had a four-person team doing the development work. 

Our team handled the deployment in-house.

Kafka is an open-source solution, so there are no licensing costs. There are third-party companies who support and provide add-ons to Kafka, but we didn't need to use any of those. Confluence, for example, provides plug-ins for Kafka. 

There were other solutions, like Apache MQ, but there were a number of components we looked at that were based around being a message bus, and Kafka was the winner from that review work.

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 utilize Apache Kafka in several areas, including financials, logistics, and client management to name a few.

We used to lose some of our messages when we integrated them in bulk, this solution has stopped that happening.

It is a useful way to maintain messages and to manage offset from our consumers. 

I would like to see an improvement in authentication management.

We have been using the solution for around four years.

The stability is good; the solution operates on our clusters without a big impact.

It is easy to scale.

We used to use a different solution, but our increased throughput meant we needed a product that would allow for a larger queue.

The initial setup was complex for us because we built it internally. This meant that full deployment took around a month.

The implementation was carried out in-house.

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.

It's a combination of an on-premise and cloud deployment. We use AWS, and we have our offshore deployment that's on-premise for OpenShift, Red Hat, and Kafka. Red Hat provides managed services and everything. We use Kafka and a specific deployment where we deploy on our basic VMs and consume Kafka as well.

We publish or stream all our business events as well as some of the technical events. You stream it out to Kafka, and multiple consumers develop a different set of solutions. It could be reporting, analytics, or even some data persistence. Later, we used it to build a data lake solution. They all would be consuming the data or events we are streaming into Kafka.

With Kafka, events and streaming are persistent, and multiple subscribers can consume the data. This is an advantage of Kafka compared to simple queue-based solutions.

We are still on the production aspect, with our service provider or hyper-scalers providing the solutions. I would like to see some improvement on the HA and DR solutions, where everything is happening in real-time. 

Kafka's interface could also use some work. Some of our products are in C, and we don't have any libraries to use with C. From an interface perspective, we had a library from the readies. And we are streaming some of the products we built to readies. That is one of the requirements. It would be good to have those libraries available in a future release for our C++ clients or public libraries, so we can include them in our product and build on that.

We've been using Apache Kafka for the past two to three years.

Kafka is stable. It's a great product. 

We did some benchmarking, but we are still looking further to scale up some of the benchmarking and performances. So far, it meets all our business requirements. We are just developers, so everything goes to the clients, who will deploy it at their scale and use it for their end customers. So were are looking at it from a developer's perspective. Those who are developing the products are working on this.

We haven't really contacted technical support, but some of our clients have subscribed to support from the vendors. We generally look for open-source solutions. From there, we try to figure out if there are any issues. There's a good online community where you can ask questions.

We were able to deploy and use it with no problems for our use case. We didn't find it so complex. We work with so many applications, databases, Postgres, and so many other things, so we could manage it easily. We deployed Kafka in a few hours. We have an infrastructure team and DevOps. Those teams are pretty capable, and they've completely automated the whole deployment. It always takes time the first time you upgrade any application, not just Kafka. We might discover some issues, such as configuration, parameters, compatibility, etc. Once that becomes standard, it is stable, and then they only need to replicate it to the different environments or different developers groups. We have a sophisticated process.

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. 

I was planning to use the tool for real-time analysis in terms of data processing and real-time analytics workflows. The real-time IoT data comes through with a few challenges, and that is for one time, so it is more like a Kafka topic. I want to actually use multiple Kafka topics where one of them can be directly fed into the data pipeline, another one can be fed into the real-time alert system, and the next one can be fed into machine learning.

The most valuable features of the solution revolve around areas like the latency part, where the tool offers very little latency and the sequencing part. The sequencing part actually helps to aggregate things in a way that I don't need to write another function or kind of sequence it, and I write an aggregate function to figure out the maximum value in the last ten samples.

One complexity that I faced with the tool stems from the fact that since it is not kind of a stand-alone application, it won't integrate with native cloud, like AWS or Azure. Apache Kafka has another mask on it, so if users can have a direct service, like Grafana, that can actually be used as a stand-alone tool with Grafana cloud, or you can use a mix of AWS and Grafana, so there is not much difference with it. I expect Apache Kafka to have Grafana's same nature.

The product's support and the cloud integration capabilities are areas of concern where improvements are required.

I have been using Apache Kafka for a year.

Stability-wise, I rate the solution an eight out of ten.

Scalability-wise, I rate the solution an eight out of ten.

Around four people in my company use the product.

I did not interact much with the product technical support team. I did not have dedicated support that responded to all my queries since I was using the product's free version. I rate the support a seven out of ten.

Neutral

I have worked with Databricks. I use Databricks and Apache Kafka simultaneously.

The product's deployment phase is neither complex nor straightforward. As the software has evolved a lot, users can actually keep it even simpler by opting for a plug-and-play model.

The solution is deployed on an on-premises model.

The solution can be deployed in two or three days.

I was involved with the tool's installation process.

I cannot comment on the tool's ROI since I did not use it for production purposes.

I was using the product's free version.

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.

Our primary use case of this solution is for data integration and for real-time data consumption. I'm a senior staff engineer for data and infrastructure and we are customers of Apache. 

I love the simplicity of the library and the design as well as the architectural concept which is like horizontal scaling.

When compared to other commercial competitors, Kafka doesn't have the ability to scale down, the elasticity is lacking in the product. The other issue for us is the delayed queue, which was available to us in the commercial software but not in Kafka. It's something we use in most of our applications for deferred processing and I know it's available in other solutions. I'd like to see some tooling support and language support in the open source version. 

I've been using this solution for four years.

The stability is good. 

The solution scales horizontally and scales better than its competitors. We have around 400 to 500 microservices consuming this cluster and the company has around 600 employees. We have four different verticals, each with around 100 engineers with 100 to 150 microservices. 90% of the microservices have a touchpoint with Kafka.

I think the community is very good and will respond if you raise a ticket. We also use external third-party libraries that were built in GitHub. It would be good to have some direct support from Apache.

Four years ago we were using Rabbit MQ but we switched to Kafka because Rabbit was designed for a very narrow use case. It became difficult for us to run and maintain that server and our client libraries. We had a huge outage, so we shifted to Kafka because of the simplicity in the architecture.

The initial setup was simple although we had a couple of hiccups. It took around a week but that was several years ago and we haven't had any problems since. Our team carried out the deployment and we currently have a few engineers who deal with maintenance. 

We are currently using the open-source version. 

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

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.