We find that the best features include using the CDC functionality with the connector to take the data from our SQL database and publish it to many consumers. Any changes enable us to easily publish changes about their domain business objects without too much code and work from domain teams. In this way, we can more easily provide a very robust layer of API and events.

The second use case is easier projection of data. We found that many teams were struggling to create projections and read stores with regular event buses, and Apache Kafka on Confluent Cloud helped us because of all sorts of features, such as the log architecture they have, and other features. KSQL also helped us there.

When order is more important, we rely on Apache Kafka on Confluent Cloud.

The benefits that I have seen from having a real-time architecture include better velocity for developers. That is the main one. Instead of developing many of those capabilities in each team, we can rely on Apache Kafka on Confluent Cloud to provide those functionalities we want, and the teams can focus on their own business instead of providing all sorts of APIs and dependencies to other domains, allowing everyone to run faster.

We find that the best features include using the CDC functionality with the connector to take the data from our SQL database and publish it to many consumers. Any changes enable us to easily publish changes about their domain business objects without too much code and work from domain teams. In this way, we can more easily provide a very robust layer of API and events.

The second use case is easier projection of data. We found that many teams were struggling to create projections and read stores with regular event buses, and Apache Kafka on Confluent Cloud helped us because of all sorts of features, such as the log architecture they have. KSQL also helped us there.

I think what I would improve about the solution is the cost, mostly. From my standpoint, it's the cost. From an engineering perspective, it works really well.

There's always room for improvement. One more point is sometimes it's more UI-related issues. Some of the more high-end features are more complicated to execute. But overall, it's a good product.

I have been using Apache Kafka on Confluent Cloud for around a year, maybe two.

When it comes to assessing the impact of the automated scaling features, we don't measure it, but it's part of our technology stack selection criteria - it's pretty much a must today.

We don't want to increase the headcount in our DBA team. They are the ones managing all our databases, queues, and data sources. So for us, having a very thin layer of management is critical, and we sit with other compute. That's very important for us because headcount is the most expensive part.

We looked at other products, specifically other Kafka providers. We have Apache Kafka and AWS. We looked at self-hosting it, but we wanted Apache Kafka on Confluent Cloud.

Neutral

We were looking for specific use cases. We compared different Kafka solutions, not necessarily competitors. We have a message bus already. We wanted the log capability, mostly.

The setup was easy enough. We got a lot of support from people at Confluent and AWS as well.

Regarding ROI in any capacity, whether it's savings from employees or cloud, the ROI was very significant. Although, specifically with Apache Kafka on Confluent Cloud, it was a bit more challenging to increase adoption because it's very expensive. So we had to pick and choose where we implemented to make sure that ROI is positive.

I don't remember the exact number because it's been a while since we did a pricing talk, but it was expensive.

They charge per topic and other resources. Because we are very cost sensitive, we want to approve it and make sure people don't just use it unnecessarily.

I would give Apache Kafka on Confluent Cloud a rating of seven out of ten.

For somebody who's shopping around, looking in this space to decide what to purchase, Apache Kafka on Confluent Cloud is a market leader. It's almost the first choice.

Going with AWS Apache was also very compelling to us because it's very quick to enable stuff in AWS and try it. I would start with those, but first understand if this is actually what you need. There are other much cheaper solutions that serve other use cases, and sometimes people can mix those and just pick the wrong product.

Overall, I would rate Apache Kafka on Confluent Cloud a nine out of ten.

Neutral

Positive

We use Apache Kafka on Confluent Cloud for streaming large volumes of data in real-time. It's employed in scenarios such as handling events from various countries and streaming them efficiently for our clients. 

We also utilize it for data analytics and in client versions for topic creation, consumer consumption, and ACL provisioning.

Apache Kafka on Confluent Cloud provides an enhanced level of reliability and resources compared to Apache Kafka alone. It offers more features which are beneficial for our clients, including cluster linking, schema registry, error handling, and dead-letter queues. It significantly improves customer and publisher satisfaction, especially with topic integration and data streaming.

Apache Kafka on Confluent Cloud is more reliable and frequent to use compared to Apache Kafka. Its features such as schema registry, cluster linking, error handling, and dead-letter queues provide significant benefits. It also offers enhanced visibility and integration for data streaming, helping clients and customers use it efficiently.

Some areas for improvement in Apache Kafka on Confluent Cloud include issues faced during migration with Kubernetes pods. This aspect could be smoother to better support migration processes.

I have been working with Apache Kafka on Confluent Cloud since September 2022 after joining my current company in June 2022.

The solution is stable and monitors activities, ensuring reliable operations. It provides alerts for unusual activities that allow us to take proactive actions.

Confluent Kafka's scalability is rated eight out of ten. It's capable of horizontal scalability by adding more consumers to handle high message throughput.

Technical support for Confluent Kafka is very good. Their efforts to provide timely solutions to bugs and defects have been excellent.

Positive

Previously, solutions such as Red Hat AMQ and Google's PubSub were considered, but Apache Kafka on Confluent Cloud was ultimately chosen.

The initial setup is straightforward with provided resources and documentation. I started working with it in the middle stages, not from the initial deployment.

Deployment can be done by two members based on requirements, and a single DevOps engineer can handle both deployment and maintenance.

I'm not sure about the pricing of Apache Kafka on Confluent Cloud.

I studied the PubSub and AMQ platforms yet did not have hands-on experience with them since Confluent Kafka was already implemented in my company.

I recommend new users start by going through the Confluence page and training to learn about Confluent Kafka's features and differences from Apache Kafka.

Overall, I think it's a good experience. Apache Kafka can be quite complex and difficult to maintain on your own, so using Apache Kafka on Confluent Cloud makes it much easier to use it without worrying about setup and maintenance.

The solution is expensive. 

I have been using Apache Kafka on Confluent Cloud, the platform's main product, for about three months.

I haven't experienced any major stability problems or bugs.

The tool is scalable. I've found Apache Kafka on Confluent Cloud to be very scalable. We've been able to scale up the volume of data we're handling without any issues with performance.

Setting up a new cluster on Apache Kafka on Confluent Cloud is pretty easy. You can click a few options, and there's also integration with other tools to create the necessary resources. However, there are some caveats to be aware of. The type of networking setup you choose can impact your functionality and visibility in the platform. For example, if you have a public cluster, you'll see more metadata information in the console than a more restricted network deployment.

Regarding pricing, Apache Kafka on Confluent Cloud is not a cheap tool. The right use case would justify the cost. It might make sense if you have a high volume of data that you can leverage to generate value for the business. But if you don't have those requirements, there are likely cheaper solutions you could use instead.

I rate the solution an eight out of ten. 

I use the solution in my company mostly for log-related purposes.

The solution's most valuable feature is that we used it during the building of data pipelines. From that, we read, wrote, and managed all the processes if there was any event happening. So, in real-time, we are just tracking how many users or logs are coming into our company.

Maybe in terms of Apache Kafka's integration with other Microsoft tools, our company faced some challenges. With other products, specifically non-Apache ones or Microsoft, our company had created some wrapper classes around them.

The integration issues of Apache Kafka revolve around other products like Tomcat, other web servers, or Node.js and .NET Core-related areas. After creating some wrapper classes around them, we were able to get the information we wanted.

My company has not used any AI features in the product, but I would like to see AI features in the product because they currently help generate real-time content. AI can help our company slowly create some LLM. My company is not currently implementing any AI features, but I see that there will be a lot of opportunities to use such functionalities. I have experience with Databricks, for which I have used AI.

I have been using Apache Kafka on Confluent Cloud for more than a year.

The tool's reliability and scalability are good.

We did not have to ask for much help from the product's support team since we are still exploring the solution. My company mostly looks at forums or blogs, and so we are able to solve any issues associated with the tool. I rate the support an eight out of ten.

Positive

My company has previously used Redis, ActiveMQ, and RabbitMQ. Apache Kafka is better than RabbitMQ. The installation, configuration, and real-time streaming are good with Apache Kafka. Usually, the tool is configurable.

The product's installation phase is pretty straightforward for us since we know how to use it.

Initially, the solution was deployed in a few hours, but after that, we were able to install it quickly.

I consider that the product's price falls under the middle range category.

I have not exactly used the tool for data management, but I have used it for log aggregation.

The tool does not need to be maintained currently, but we are still exploring how we can make it perfect. My company's client is asking for a little bit more from the tool, so we will deploy it. Right now, we have our own products, and in those tools, we are just seeing how we can use Apache Kafka.

My company has not used any AI features in the product.

I rate the tool an eight out of ten.

It's basically four bands of use cases, where we publish data on Kafka topics and stream it across microservices.

Some of the retention policies of Kafka topics have been most beneficial for data management specifically.

Based on our experience, there are different use cases where data needs to be handled in different ways. Sometimes we want to get rid of it once it has been consumed, or we have to store it for a longer period. 

Kafka provides handy properties that allow us to directly configure the data, whether to keep it or discard it after use.

I feel the streaming speed, the way messages are processed, and some of the topic features like partitions and offset management are quite handy.

There's one thing that's a common use case, but I don't know why it's not covered in Kafka. When a message comes in, and another message with the same key arrives, the first version should be deleted automatically. 

We want to keep only one instance of a message at any given time, the latest one. However, Kafka doesn't have this functionality built-in. It keeps all the data, and we have to manually delete the older versions.

 So, I would like to have only one instance of messages, based on the keys. If the key is the same, there should always be the latest message present instead of all versions of that message.

I have been using it for three years. I use the latest version. I work with v3.6.

I would rate the stability a six out of ten. When it's good, it works fine. But as soon as traffic increases and the number of topics on the cluster exceeds a certain limit, it becomes unresponsive. 

We then have to get rid of Kafka topics, but even that's not easy because the whole site becomes unresponsive. We don't have easy dashboard access to remove unnecessary topics. That's one issue.

I would give it an eight out of ten for scalability. It's scalable, but there's room for improvement in reliability. When we scale up at the pod level, reliability goes down due to mismanagement of offsets, leading to data loss. Then, there is mismanagement when we scale it up, and then there is a point where we want to scale down because traffic is less. 

During scale-down, we also often see data loss. They can work on improving this.

We currently have large enterprise business as our customers.

I would rate my experience with the initial setup of this product, a seven out of ten where one is difficult and ten is easy.

I have not faced any difficulties or challenges while setting this product up. They have proper documentation, so it's easy to go through it and set things up.

It's the cloud solution, so it's deployed on the cloud in our customers' organizations. And they use Confluent Cloud. 

It's taken care of by different teams.

Overall, I would rate it an eight out of ten. 

I would recommend it because everything is well-documented and straightforward.

We can install Kafka directly, but we don't have direct access to the data on Kafka. So it's good to have tools like Kafka Magic or Kafka Tool to access and visualize the data.

Whenever you need to handle a huge load of real-time data processing, Kafka is useful. We currently use it for an output management system for insurance, where the system receives data in a fixed amount and has to process it in several steps. 

We manage these steps with Kafka because the load can be quite big, with millions of XMLs coming into the system that need to be processed in near real-time.

The most valuable feature to me is it scales really well when there is a data influx, irrespective of the peak time. It can handle both large and small amounts of data. 

Also, the state-saving feature is very much appreciated. It allows me to rewind a certain process if I see an error and then reprocess it.

The administration port could be more extensive. Additionally, managing the states of certain events could be made easier, perhaps with automatic rollback instead of having to program it manually.

I have been using it for a few months. 

For us, the stability has been great. No complaints here.

Scalability is great. We haven't gone into production yet, but so far, it scales very well.

I would rate the scalability a nine out of ten. There are maybe a dozen or so people who really have to interact with Kafka.

The setup was straightforward. Confluent Cloud takes a lot of the work out of the user's hands, and it's easy to set up. We're quite happy with it.

We didn't need any help. We have one developer, and that's it.

The benefit is that we can offer clients cloud streaming for data processing, which we host for them. They benefit from the speed and reliability.

Since we are in development, we have no license. We will need one eventually. The price isn't low, but it's not too high.

I evaluated other options for workflow solutions like Camunda, and other messaging services, like NQ series.

Overall, I would rate the product an eight out of ten. So far, we haven't found any obstacles, and we're happy that it's been straightforward and problem-free.

The product is very good, but if you need to set it up on your own, there's quite a bit of work. That's why we chose Confluent as a partner who has made it tailored for us. That's surely a good way to start. When you have more experience, you can try setting it up on your own.

In my company, we are not using the tool for analytics and it is more for CDC processes, so we change the capture processes. It is used to extract data from a database and make it available in other parts of our systems or produce events that inform us of data updates.

There are some premium connectors, for example, available in Confluent, which you cannot access in the marketplace, so there are some limitations. From Confluent's point of view, I understand where they come from, but I believe its deployment model is a little strict. Confluent Platform can be installed in your own infrastructure. Even if you install Confluent Platform on your own platform, you need to use the components that Confluent offers. Otherwise, the support is very limited, and I think this is an idea of improvement for Confluent. Confluent is pretty solid, so I don't have much in terms of improvement.

I have experience with Apache Kafka on Confluent Cloud.

Confluent gives you 99.99 percent availability, so I rate the tool's stability a nine out of ten.

If you use Confluent Cloud, the platform's case can go up and down depending on your needs, and it is very easy from the point of view of storage as well because if you are getting more advanced, it basically scales up your storage. If you are given a number of events using your storage device, it is very easy. If you use Confluent Platform, you have a little bit more manual management there, although being a product that assists you with some side components like CFK.

With Confluent, if you have its tools, I rate the support an eight out of ten, but if you have mixed products, then it is a six out of ten.

Neutral

I have experience with Confluent Cloud and Amazon MSK. With Confluent Cloud, we are really happy with the ecosystem that is made available, along with the connectors, SQL, DB, and other such aspects. The tool can be provided in a very easy way, and it was really effective for the type of activities that we do. The tool presents quite a range of possibilities for integration between different sources and things.

While you use Confluent, all of the services that are needed to manage the enterprise-level EDA are available to you, and you have an integrated schema registry, together with the entire schema registry, and you have a portal for publishing your schema. You can do routing and filtering by configuration. You have CFK, which allows management of your cluster, allows monitoring of your cluster, and allows you basically to connect to the managed connectors on your cluster. Confluent is a full-fledged platform for an event-driven architecture that can be deployed at an enterprise scale, while Amazon MSK is just Kafka as a service from AWS.

A part of the delivery team does the setup, but it was pretty easy on both sides, as with AWS and Confluent, the team didn't have much trouble.

The main return on investment was in the maintenance space because going for Confluent Cloud means you remove all the platform management that you have in terms of these resources that can be allocated to other tasks, where the tool takes basically ownership of all of these. We saw, at the end of the year's end, improvements that were substantial, especially when it comes to the need to resolve issues, as we can deploy the minimum team possible for Confluent because the support model allows for the Confluent team to take ownership of the issue. With AWS, the tool's team supports us, but we have to deploy the right people and take them out of all other initiatives. The most important part is the cost related to platform maintenance and issue resolution.

Using Confluent, you have more licensing prices to account for when you calculate. I think the pricing is fair, but Confluent requires a little bit more thinking because the price can go up really quickly when it comes to premium connectors.

Speaking about data security and privacy requirements, I would say that there are some BAA or legal agreements in the tool. We did not have issues in terms of security or breaches, but before any adoption, with PII or PHI type of data and before having this data flowing to other clouds or other platforms, the BAA needs to be signed because of IPAC.

Confluent Cloud handles data volume pretty well.

If you are starting to deploy a fully-fledged ETA platform where you do not just have information streaming and go for CDC, and you have some legacy systems that have to communicate on your systems, then I suggest you go for Confluent Cloud.

I rate the tool an eight out of ten.