I would rate the product's scalability a nine out of ten. My company has around 220 users for the product. 

This solution is easy to scale.

I haven't used any other Data Management Software yet, but in my opinion, I am quite sure that if the configuration of the hardware remains the same, then other software solutions might be able to perform better.

At times with SAS, simple processes can take a lot of time. If you look at other solutions such as Oracle, or SSIS which is in Microsoft, they are database solutions that can handle this kind of data and can load it faster.

For Data Management, we use only three to four people who are responsible for the ETL process, but overall there are always 20 to 25 concurrent users, who are mainly IT administrators or IT managers, and Business Analysts who are creating new dashboards or who are doing some ad-hoc analysis. 

It is a very vast program and you only use what you need. As much as you can use it, it is as scalable as you want to make it. There are good options for making use of it from individuals to an enterprise solution. If that's the meaning of scalability, then it can grow with you. Also, I know they bring out new releases of the software which is scaling in even another way. 

SAS Data Management is very scalable. I get different solutions in one application to do all the activities you require for achieving your tasks. Based on that, we can configure or place this particular solution and that can be built it into one application. It's a very scalable application.

In our organization for the project I'm working on there are 2000 users. They are in separate roles - they have the developer role who does the management activity. There is the operational role who is monitoring and executing the data sets. Then there is the business user who uses the particular data which is required for the application input or for the reporting purpose.

In terms of maintenance, we have an operational team and a development team who do all the maintenance work. This includes the deployment required for the latest releases and the changes in the application. If those are hot fixes that are released then deployment is required. That only matters when it is an enterprise level user. This is not a frequent activity, but it is required. On the data level, as data changes or something is happening to the data or deliveries are not appropriate, then of course, the operational roles come into the picture.

We are already on a plan for using SAS Data Management on a broader scale. Initially, when we started, it was on a particular set of people. Then we increased for the business users, then as the set of businesses grew we created another level, so that on a business user level, they can also do the exploration in the data and provide the reports to the end user as well. So there are now four levels. We have the developer, operations, business users, and end users. These four levels are expanding the scope.

It is scalable. We are using it across the globe. We have a presence in 18 countries, and we are using it across the globe for our reports.

In terms of its users, we have more than 200 users across the group. Its users are quite tech-savvy. They write programs and extract the reports. The consultants help them with the development of the project and reports, and then we use them and modify them as per our requirements. We have a small team of 10 to 12 consultants. Some of them are outsourced, and some of them are in-house.

Very large data stores require a powerful server to run the data profiling reports for all of the rows in the database. My estimate for “very large” is a database >= 200 million records.

• In the server, from time to time, depending of the type of job the users submit to the server, we have to increase the amount of memory in the server. However, we have a limited number of users that can deploy jobs to the server.
• The desktop client, when running a job, consumes resources of the client machine and of the database being accessed. This being said, the scalability depends of the user machine and database where the information is stored.
• Like many others tools that provide the same functionality, in order to perform the required analysis, the data must be transferred to the product and this step consumes time (network) and resources (memory and cpu). When the amount of data is very large (millions) the amount of time and resources is considerable high so the way to go is to analyze data samples and extrapolate from there.

There are very few limitations to the scalability of the product. Organizations with hundreds of thousands of customers or millions of customers use this solution. It scales very well. The support is good.

No, however we spend time analyzing our system upfront.

SAS is strong scalable, I have used on very little server that during the time have grown to huge. I can say that with SAS this is not a problem.

No, high integration level with SAS