The product scales well. It's fine to expand if needed. 

Many teams use Spark. For example, we have a few kinds of pipelines, huge pipelines. One of them processes 300 billion events each day. It's our core technology currently.

We do not plan to increase usage. We keep our legacy system on Spark, and we are now discussing Flink and Spark and what we would prefer. However, most of the people are already migrating new systems to Flink. We will keep Spark for a few more years still. 

We have more than 100 Apache Spark users in our organization.

The solution is scalable, but adding new nodes is not easy. It will take some time to do that, but it's scalable. We have about 20 users using Apache Spark. We regularly use the solution.

It is a very scalable solution. Scalability-wise, I rate the solution a nine out of ten.

There are no different numbers of uses for Apache Spark in my company since it is used as a processing engine.

It serves as a data node, making it highly scalable. It caters to a user base of around five thousand or so.

It is a very scalable solution.

In our company, there are users of Apache Spark, and then there are users of the applications that were developed with it.

Currently, my company does not plan to increase the use of the product.

The solution is scalable. We used a load balancer at each tier, with multiple instances of the services running. 

It's all scalable and relevant. We didn't have a lot of issues and have been monitoring the traffic flow. 

We even projected the requests for the next two to three years and created scalable instances accordingly.

There are many users of Spark in our organization. For example, many customers are using Spark, often in conjunction with requests from third-party vendors. They frequently use Spark plug-ins as well.

I have found Apache Spark to be scalable.

Apache Spark is a scalable solution. More than 50 to 100 users are using the solution in our organization.

We have 45 Apache Spark users. I rate its scalability a nine out of ten.

About 70-80 percent of employees in my company use the product. 

The tool is very scalable. I rate the scalability a ten out of ten. Approximately 30 users are using Apache Spark in our organization.

We have found the scalability to be good. If your company needs to expand it, it can do so.

We have five people working on the solution currently.

The solution is scalable. 

It is scalable, but we don't have the need to scale it. 

It is mainly used for reporting big data in our organization. All teams, especially the VR team, are using Spark for job execution and remote execution. I can say that 70% of users use Spark for reporting, calculations, and real-time operations. We are a very big company, and we have around a thousand people in IT.

We will continue its usage and develop more. We have kind of just started using it. We finished this project just three months ago. We are now trying to find out bottlenecks in our systems, and then we are ready to go.

The solution is highly scalable. All of the technical guys use Spark. Our product is used by many people within our customers' company.

The product is enormously scalable.

The solution is highly scalable. I rate it a perfect ten.

Around 15 data scientists are using Apache Spark in our organization.

We have a small business. Around four people in my organization use the solution.

When it comes to the scalability of Spark, it's primarily a processing engine, not a database engine. I haven't tested it extensively with large record sizes.

In my organization, quite a few people are using Spark. In my smaller team, there are only two users.

This is a very scalable solution from our experience.

Apache Spark is scalable. However, it needs enormous technical skills to make it scalable. It is not a simple task.

We have approximately 20 people using this solution.

As long as you do it correctly, it is scalable.

Our users mostly consist of data analysts, engineers, data scientists, and DB admins.

It ensures outstanding scalability capabilities.

We've had no issues with scalability.

It's linked to stability in our case it's takes time to evaluate what is the correct size of the cluster you need. It's very important to always add to you jobs monitoring to be able to understand what's the problem. We use datadog as monitoring platform

The cloud version of Spark is very easy to scale.

The scalability of the solution is very good. If a company has to expand it, they can do so.

Right now, we have about six or seven users that are directly on the product. We're encouraging them to use more data. We do plan to increase usage in the future.

I have not tested the scalability yet.

In my company, there are two or three people that are using it for different products. But right now, the client I'm engaged with doesn't know anything about Spark or Hadoop. They are a typical financial company so they do what they do, and they ask us to do everything. They have pretty much outsourced their whole big data initiative to us.

No issues.

It is very scalable. You can scale it a lot.

It is not scalable. Scalability is one of the issues.

In our team that works on this, we have approximately 10 people.

I've had no issues with the scalability.

No.

There were no issues with the scalability.

It's scaled without issue.

No.

We are using Apache Spark across multiple nodes and it is scalable.

We have approximately five people using this solution.

There have been no issues with the scalability.

The solution is scalable. My understanding is version 3.0 has renewed scaling capabilities and will be able to do so automatically.

No I did not as of now, it is quite scalable. Using simple scripts you can add as many workers as you want.

I haven't had any scalability issues. It scales better than Python and R.

The scalability is very good.