The solution is scalable to the cluster size and Azure storage. 

Scalability is rated an eight out of ten. 

It is not really scalability but more about the combination of the structure, consolidation, and different formats we can split and merge. We do a lot of things while storing the target operational model. Snowflake is more flexible and scalable in that regard. 

The solution is scalable with no issues from a computer perspective.

The scalability is fine. We had thousands of devices and were sending data infrequently, so that worked for us. If the amount increases, the windowing function and job schedule may not perform as expected.

The solution is good for scalability.

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

My company has a team of 50 to 60 people who use the solution.

The solution is scalable. In this particular ecosystem, there is no one else who can catch up with Databricks for now.

It's very scalable. I have a project that started as a proof of concept on connected cars. We had 100 cars to track at first - just for the proof of concept. Now we have millions of cars that are being tracked. It scales very well. We have terabytes of data every day and it doesn't even flinch.

The product is scalable. We're a small organization with 12 users, and we don't currently have any plans to increase our usage.

It's a scalable solution. Very few people are using this solution in our organization. Most don't have the skill.

There are around 30 to 35 users in our organization. 

The solution is scalable. There are five people using it in our organization.

This is a very scalable solution. We have twenty-five data engineers that use it, and we may grow our usage.

I would rate the scalability an eight out of ten.

Databricks is very scalable because it's very easy to spin up multiple clusters, but the cost of doing that is tremendous. I'd rate its scalability nine out of ten, but you'll pay for it.

I would rate the scalability of this solution a nine, on a scale from one to 10, with one being the worst and 10 being the best. I would say there are around 2,000 to 3,000 users of this solution in our organization.

We have found that part of the beauty of this platform is that it is easy to scale and expand.

The system is extremely scalable. It's one of its greatest features and a big selling point. If a company needs to scale or expand, they can do so very easily.

We require daily usage from the solution even though we don't directly work with Databricks on a day to day basis. Due to the fact that we schedule everything we need and it will trigger work that needs to be done, it's used often. Do you need to log into the database console every day? No. You just need to configure it one time and that's it. Then it will deliver everything needed in the time required.

Databricks has high scalability. Most of my work on the solution has been in the pharma space, which has massive data sets, so it's a nine out of ten, scalability-wise.

The tool is scalable. We can make it available for a larger audience.

Databricks is absolutely scalable, and I'd rate scalability at eight out of ten. We probably have between 60 and 100 users in our organization, and we hope to increase usage in the future.

Scalability is no problem. At the beginning, we created a cluster, for example, and if we need more performance in the future, for example, or to accelerate the training, we can change the cluster. It's quite straightforward. 

We have five people using the solution. 

In one or two years, we'd like to promote the solution to clients and increase usage. Right now, the way it is used is limited. I know that some banks and aeronautics companies use it.

Databricks is scalable.

I rate the tool's scalability an eight out of ten. 

This is a scalable solution. 

The solution is hosted on the cloud. It supports high scalability features.

10-20 users are using this solution.

Databricks is scalable. You can use the power of the cloud to scale your cluster size, either CPU or memory. The data doesn't work like a standard database, so you don't have it based on files, and you don't copy the data. It's super scalable. It's only the computing that you have to scale with the data.

We probably have 40 users with roles like developers, business analysts, and data scientists. We have big plans to increase the usage and have more departments using it.

Databricks is based on Spark, which is based on Scala. These languages aren't easy to handle, and it's challenging to find people who know them well. At the same time, a couple of other vendors that work on top of Databricks are low-code platforms. We have to work around Databrick's lack of scalability by using low-code platforms that work on top of Databricks to give us scalability.

It is a scalable solution, both vertically and horizontally, which is good. My organization is big, and we have a lot of users. In my department, we have about 15 people using Databricks.

Databricks is a scalable solution. It is the largest advantage of the solution.

Databricks relies on scalability and performance. Every cloud vendor prioritizes scalability, high availability, performance, and security. These are the most important reasons to move to the cloud.

The scalability is quite good. A company that needs to expand it can do so with ease.

We only have four people on the solution at this time. The front-end users never use the product directly. The companies aren't that big here. If the economy improves, we'll likely have more of a need for the product.

The salability of Databricks is good. However, if I want to use the higher clusters and high concurrency clusters, you will need to wait more time to spin up the clusters. 

We have different teams. Among them, I'm part of the data analytics where, our team, almost 10 people are using it. But I'm not sure about the rest of the teams.

We are using Databricks extensively. We have a team of 10 using the solution.

I have not used autoscaling because it consumes a lot of money and because my experience has been alright. In some cases, though, it is tied to the quota of the underlying infrastructure. I have not tested the scalability to its fullest extent, but with the workloads I run, it has been fine.

Databricks is scalable.

I rate the scalability of Databricks a nine out of ten.

I would rate the scalability of this solution an eight out of 10, with one being not scalable and 10 being very scalable.

There are three people using this solution in our organization.

The scalability of this solution is good. In our organization, users include analysts, data engineers, and data scientists.

The solution is easy to scale horizontally and it has a useful auto-scaling feature. For vertical scaling, you need to bring the system down and make some adjustments.

On my current project I have a team of 30 members under me, including data engineers and data science people. Our data science, engineering, and MLOps projects are expanding, so we are planning to do some vertical scaling to increase the team size to over 100 members. In our company, we are trying to certify more and more people in Databricks because it's cloud-agnostic. 

The scalability of Databricks is good as long as you have a data lake, and it's easy to scale.

We have approximately 50 users using this solution in my company.

It is a scalable solution but this depends on the platform that is being used. If you use a cloud platform such as Azure, it offers scalability. However, some platforms will not support scalability using Databricks.

We have around 20 users in our development team using Databricks. 

It was simple to make Databricks scalable. We found that we could set up an alert to tell us if we needed more resources, money, or time from our team. We're alerted when the system detects some trigger for any use of the instance. If you have another alert from your side, that would be extremely useful because it takes a lot of time to develop that kind of trigger. 

The solution is scalable. 

Absolutely, seamlessly, and massively scalable, within only budgetary limits. Also, the product itself offers real-time efficiency and optimization recommendations. 

Scalability is quite good because we just used 128 GB of resources. It's quite easy to scale.

The solution is scalable, and we are trying to implement more use cases with Databricks in our organization as we advance. I rate the scalability an eight out of ten.

I have approximately 20 users using this solution in my organization. We have plans to increase our usage in the future.

I would rate the tool’s scalability a seven out of ten.

Databricks is highly scalable.

We have 200 people using the solution in my organization.

Databricks is scalable, regardless of which cloud provider is being used. It is supported on Microsoft Azure, AWS, and they have their own cloud as well.

For a small workload, Databricks may not be worth the costs. However, for larger workloads, Databricks is a very good solution.

In my previous organization, there were between 10 and 15 users.

Obviously running on top of Spark, ensures fast processing and elasticity for coping with big data volumes, up to 2 petabytes. You can spin up the cluster very quickly, and shut it down. It's elastic.

We have about 200 employees but it is only a small group using Databricks. We are at the beginning so scaling is not something we have had to do.

Databricks is scalable. The only issue is how much money you have for it. For example, if you need to run 100 servers, there's an eight-course with 256 gigabytes of RAM. You run out of money easily. It's charged to your credit card or your account, and you'll have to pay for it if you don't plan for that in advance.

It's a scalable solution. 

The product is quite easy to scale and increasing the number of users is quite simple. 

We have not really gotten to the point of scaling and testing scalability at this point. We only have two people involved with the product. One is a data scientist and one is a data engineer.  

The scalability depends on the project. At present, around 20 people use the solution in my company.

The scalability of Databricks is good.

We have approximately 500 users using this solution in my organization.

It's scalable.

Scalability is really good.

Scalability of the solution seems very easy to achieve. 

We started using Databricks with a small PoC application, and then we developed it into a larger one. It's scalable, and it's a simple process to scale.

We have eight people in our team who are using this solution. We do not plan to increase usage at this time.

Databricks is scalable.

Scalability is one of the main features of Databricks. We have used datasets that are one hundred megabytes in size up to one terabyte, and we can manage, so it's easily scalable.

We have a large company with between 400 and 500 people using this solution.

Both the scalability and the stability of Databricks is good.

The solution is scalable, and there are approximately seven developers and two DevOps employees utilizing the solution.

Databricks is scalable.

Our implementations have been small and we haven't needed to scale as of yet. 

Databricks can help you to build a data lake, and it's something that they need to help make more popular. People are slowly understanding it because if you look, there are lots of data lakes that people are trying to create. I'm not intimate with it, but the concept seems complicated. I think they need to write up something where videos can explain it better. What I have seen on YouTube is quite complicated for an end-user to understand.

It's hard to say how scalable the solution is. The scalability comes into play on the Spark side, not on the Databricks side.

We have about 20 people on the solution right now.

We are in the prototyping stage so we do not plan on increasing our usage yet.

The solution is scalable. However, it still needs us to manually set out the number of nodes in a cluster. It's really dependent on the application. Sometimes, when the tasks are bigger, and it gets a little difficult for us to define the number of nodes in a cluster. If the solution could allow users to set up the clusters, I think that'll be good.

Currently, we have three people using the solution. We may increase usage in the future.

Databricks is scalable. It operates three times faster than any of the other ecosystems which we have experimented on.

We have approximately five data scientists using this solution in my organization. We are a small company and as we grow, all our data scientists would be using this platform. We plan to increase usage.

In terms of scalability, I think once we put it across for larger use-cases the scalability question will really arise. So we'll need detailed information. I assume that we will be able to scale up.

I think we do not have more than 10 people working on it now. Because we are in the earlier stages of implementation, it's more like a POC now. I really don't know whether it's been open for the larger audience yet.

The scalability of Databricks is good.

We have 30 to 40 people are using this solution in my company.