Confluent is the cloud version of Apache Kafka and it is scalable.

I would rate the scalability of Apache Kafka somewhere around seven out of ten. I'm not going on the higher side because a lot of manual work is involved in upgrading Kafka. You have to estimate the overall capacity, not just in data but also in other use cases running on the same cluster. In the cloud, it is easier as you don't have to worry about the turnaround time of your cluster setup. But in an on-premise setup, you need to add more nodes, RAM resources, and storage based on the increasing data volume. 

Also, it would help if you ensured that the streaming use cases running on Kafka are not impacting other use cases like batch or archival use cases. Because of this manual activity of overall estimation, I will still keep it somewhere around six to seven. But regarding scalability in terms of horizontal or vertical scalability from the data perspective, I feel more comfortable with Kafka compared to other available streaming solutions.

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

The users of the solution are not directly involved with it. Those who use Apache Kafka in our company use it to push orders on the frontend, and then the frontend calls for some microservice, after which the microservice pushes data to Apache Kafka. Around 10,000 to 15,000 people in my organization follow the aforementioned procedure.

The scalability of Apache Kafka could improve.

We have approximately 10,000 users using this solution.

It depends on the configuration., but scalability is one of the best features of Kafka. I would rate it nine out of ten.

We use the solution in the distributed mode in multiple regions – the US, London, and Hong Kong. We have increased the number of nodes to ensure it is available to us at all times.

I give the scalability an eight out of ten.

We have around 600 people within my team using the 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.

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.

It is very scalable. It is easy to scale it. 

It doesn't matter how many users are using it. The licenses are calculated based on the number of nodes. It is not based on the number of users who are using it. We have between 10 to 20 nodes on average in an organization.

I rate Apache Kafka a seven out of ten for scalability.

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.

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

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. 

It is easy to scale.

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

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

My opinion is that Apache Kafka is a scalable solution. In our organization, there are hundreds of thousands of users using Kafka.

Apache Kafka is the most scalable solution in the market.

It is a scalable solution. We set up a category with different consumers balancing things, which works as I thought.

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.

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

The solution is scalable.

The solution is scalable.

The solution is very scalable. We started with a cluster of three and then scaled it to seven. I would give the solution a five out of five for scalability. Currently, we have 20+ employees on the technical team that are using the solution. 

We provide outsource services for other institutions. There is a whole set queue management form, and we have about five institutions, with three technical teams that use the same cluster.

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.

It's scalable.

Scaling is not really a problem with Kafka. We have used Kubernetes clusters and it is working very well. It scales up and down, almost automatically almost unnoticeable to the consumers, based upon our configuration. Kafka is just one pod inside of our cluster that scales horizontally.

We have a couple of customers that also have vertical scaling, meaning that, there's more CPU, more memory available to the Kafka pod.

Apache Kafka is a scalable solution.

I would recommend Apache Kafka for any enterprise.

The amount of people using the solution depends on the application. However, the starting point is from 6,000 to 7,000 concurrent users.

This is certainly a scalable product. There are currently 30 or more people using it but we expect to scale beyond this. It is going to be an enterprise tool within the company.

Apache Kafka is scalable.

I rate the scalability of Apache Kafka a nine out of ten.

The solution's scalability is important for our ability to have more throughput from multiple receivers. If we need more throughput it can deliver.

Apache Kafka is scalable. It is easy to add brokers.

We have approximately 30 people using this solution in my organization. They use the solution daily.

I have found the Apache Kafka to be highly scalable

Kafka is a very scalable system. You can have multiple, scalable architectures.

Scalability is one of the main selling points of the tool. The additional nodes we add give us the additional storage capacity we need. I rate the scalability a ten out of ten. The solution is used across multiple domains in our organization. I use the product daily. It’s a continuously growing platform.

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 did not have any issues with scalability.

I rate Kafka eight out of 10 for scalability. 

The solution is scalable. Deployment is speedy, but we don't have many installations. We have over a thousand users using this solution and will most likely increase the number of users because we have tested 100,000 messages per second. The solution is impressive.

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.

The scaling of the solution is quite good.

We have had no issues with scalability.

We have not had any scalability issues. The product is horizontally scalable, so adding extra hardware is all that is needed.

Kafka is designed to be very easily scalable so I did not have any trouble here.

With the containerized version we have used, we have faced challenges with the scalability.

We are a small team with a few people.

We might increase our usage in the future.

Scalability is very good. 

We have not encountered any scalability issues yet. We are growing and currently, we manage 1M events per second in Kafka.

We have not yet had scalability issues!

We have to balance the nodes when topics partition across cluster nodes. As it assumes they are of equal sizes, sometimes some nodes may not be allocated similar resources. Reassignment moves all the partitions of specified topics which may be an issue when not planned for.

The solution is scalable.

We are still in phase one so haven't yet tested scalability. That will come when we move to the second phase. We currently have around 15 users of this solution. 

We have not had any scalability issues.

We did not encounter any issues with scalability.

No issues with scalability.

In this project, we used Kafka to synchronize 28 nodes spread out nationally and it seems scalable. We plan to consolidate the 28 nodes for national integration of nodes and schemas.

It is easy to scale. This is a new project so we only have about five users right now.

It is scalable. 

I use it 24/7. We have to change both packets to Kafka and from Kafka it receives data. We retain the packet for only six seconds.

We have not had any scalability issues.

We have not encountered any scalability issues.

The scalability of the solution is very good. You can analyze system events horizontally and the cluster can be brought over to the cloud side with the Kafka user's server.

We use the solution for both small and medium-sized organizations, but also larger enterprises. Some of our clients are in the banking and financial sector.

Ah, scalability!!! We need to set up multiple servers again for handling the load, which makes Kafka not scalable, unless you subscribe to cloud services.

The solution is scalable, however, it's a 50/50 endeavor. It may require some management to build it out.

Kafka is very scalable, which is an important feature of it.

Our clients have approximately ten applications in their companies that communicate with Kafka.

Kafka is stable. Defining our user base is hard because Kafka influences the whole company, so you could say around 100 users. Kafka is a core system, so it affects all users we choose to link to the primary key.

This solution is scalable.

Kafka is a highly scalable product. We have not faced any scalability issues so far.

RESTful API implementation actually uses the Kafka Broker to publish the messages but I am not able to find it becoming scalable. Partially, the reason might be there is no load balancer for the RESTful API web server.

This is a scalable solution.