There are around 30-40 users in our company using Spark SQL.

I haven't had major issues with scaling very large datasets. I would rate it a nine out of ten for scalability. 

There are challenges to scalability with Spark SQL, but it is not too complex. Four users use Spark SQL professionally, but other developers use the solution in some minor cases. We are in the data engineering department, but our PI department also uses Spark SQL. Totally ten developers use Spark SQL in our organization.

Spark SQL's scalability is excellent. In production, there will only be a few users who are analysts using statistical reports. Queries have many joints, and one query, on average, has seven to 12 joints. There are no plans to increase usage because I'm working on creating markets. There are higher management staff analysts for the data platform, and we have plans to expand the business with data platforms.

We only have 15 users but the solution is scalable. 

The scalability depends on administrative rights. Every use case has certain allocated resources so a use case demanding scalability or extensive use can have additional resources allocated to it. 

The solution is scalable. We've found it easy to expand as necessary.

Right now, we have about 42 users on the solution. They include IT and ETL staff as well as a business analyst.

It is scalable. I use it on and off. I use it mostly daily. 

This solution is scalable. 

I have found the solution to be scalable. We have 20 people using the solution in my organization and we plan to increase usage.

Its scalability is okay. We are a big organization. 

The scalability of the solution is good.