It is a scalable solution.

In my company, around 16 people can log in to use the product at a time.

The scalability is good. Eventually, we will move our on-premise SQL database into the Azure cloud, but we're not there yet.

The scalability of the product is great. It's easy to expand. 

It's quite efficient in all the features that we're currently using it extensively. It's giving more and more function. et cetera.

Thousands of users currently using this product. For example, our clients are normally banks and the users are in the thousands. They normally use it each and every day.

It is very easy to scale the solution.

It is scalable but takes some effort.

We have more than 1000 Microsoft Parallel Data Warehouse users in our organization. It is a scalable platform.

Currently, 54 individuals in our company use it.

The tool is scalable. I rate the scalability a ten out of ten. We have about 200 users.

Apart from a few use cases that we are not able to do, the solution's scalability is fairly decent.

I rate Microsoft Parallel Data Warehouse an eight out of ten for scalability.

The solution can scale well. We're able to use it well even with clusters.

There are some tools that are more scalable than this one, and the performance is better as well. This is an area that could use improvement, especially if you have a huge set of data.

We have about 50 analysts and another 30 or 40 regular users who work with this solution every day.

The tool is scalable by design. Our customers are medium and enterprise-level businesses.

The scalability is really good. I know that we don't have to worry about scalability because one time at my former company, we almost doubled our size. In fact, the number of staff doubled and we were able to migrate very easily. The additional data was very easy to accommodate.

In my previous company, my team had 35 users. At one point, during our migration, we had about 50 users who were working extensively with the solution.

It is quite easy to scale this solution. The whole company of approximately 2,500 people uses it.

We've never had issues scaling if we needed to. If a company needs to scale, they can do so rather easily using this product.

Scalability depends on how you design the warehouse. It can be scalable, but it depends on how much data you have to put into it.

We have about six hundred users.

I think Microsoft is the largest scalable company in terms of data warehousing, elastic pools, elastic servers. Even with Power BI conducting parallel data warehouses. Those can scale up pretty large, and if you really want to, you can move into the data lakes.

I think that theoretically, it is both possible and not that hard to scale. But I am also sure that the cloud would be easier to work with for the sake of scalability if scalability is the goal.  

We have a dedicated team to work with the data warehouse which has between four to six members. The data warehouse is one of our main software solutions that we use on a daily basis. We need it for the tech that we use which focuses entirely on SQL Server.  

We are looking to scale the usage by building a business intelligence layer over the data warehouse. The tech team is searching for how to implement that with the Microsoft SQL Server technologies. I think we are going to achieve this and expand the setup. So it is scalable in various ways.  

Unlike other MPPs, such as Netezza, this requires excellent expertise in SQL to reap the benefits of using it.

I have no concern about the scalability. We have never faced any problems regarding the scalability. If we wanted to extend this, it is extendable.

It is definitely scalable. We have more than 220 users in multiple cities. Most of the people, perhaps 70%, are information technology users. The remaining 30% are business analysts who are concerned only with reports.

We do plan on increasing our usage. We recently converted all of our Tableau data, and now we are working on permissions and security.

We have not had problems with scalability.

There were scalability issues in that there is a limit to 32 concurrent queries.

The solution is very scalable.