As far as I know, it scales pretty well. The part of the problem that we have is with the way it saves the results. When it saves the result, it creates a physical copy of some of the data results and stores it. So, when we're processing, for example, 500 million rows of data, depending on the type of rules that we have and how we're doing it, it can quickly use up a lot of space. We've had some issues with some of the space and storage. It scales, but you still have to be careful how you configure it so that you don't use up all your resources. We've added a lot of disk space, and we still occasionally have problems.

Currently, we have maybe half a dozen heavy users, but we're probably going to scale that up to 20 to 25.

In terms of scalability, you see, it is on the cloud environment, which itself gives the scalable, and flexibility in terms of accommodation of the data. So if we want to scan across the data for the quality and checks, like, not on any sample data. We really want to do this profiling on the checks on a lot of data. So it is very much visible in the cloud environment, which is cloud storage, which is provided by AWS or Azure, or GCP.

I don't go into the technical side of Informatica Data Quality much since I mostly work on the procurement side and market knowledge area related to the product. Scalability remains one of the requirements of the clients who buy the product, especially for future growth. My company caters to a mixture of clients, consisting of small, medium, and enterprise-sized businesses.

Scalability is not a problem. You can't count the number of users because it's a platform that has an operational model in which we move the data. We have the data and we clean the data, but users don't have direct contact.

Scalability with Informatica had some initial challenges, especially when ingesting metadata into the data catalog due to the large volume of source systems and data. While it eventually worked, it took considerable time. Overall, I would rate it a six out of ten for scalability at the moment.

Initially, a few hundred users were using Informatica, but now it is a few thousand. 

I give the scalability an eight out of ten.

We find that scalability is not an issue and have installed it on fourteen servers.

In terms of scaling, we used the clusters, and the processing was on Hadoop side. If we needed any extra space or any service, it was just managed there, so it was outside of Informatica.

Originally, we had 20 people using the solution, and then it was reduced to less than ten.

We do use it as much as we can for its purposes. In the past, we used that for the whole ETL process with data loads, and then we moved to Hadoop storage. At the moment, we are only going to be using Cloud Data Quality and others for cleansing, standardization, and deduplication, and then using some other Azure capabilities.

The scalability of the solution depends heavily on the design approach. Regardless of the tool used, the effectiveness of the data quality framework hinges on proper design. If the framework is not well-designed, it may not deliver the desired results. I would rate it eight out of ten.

The scalability is very good. We have about 30 users and this may increase because our organization constantly needs more people, so people see the benefit because it's stable and it gives consistency. We will probably move to big data analytics and purchase their big data applications.

Compared to PowerCenter, Data Quality needs some improvement. When we are dealing with large amounts of data for extraction purposes, it doesn't always work out — often we experience some memory issues here and there. Scalability-wise it needs some improvement.

It is scalable. We have ten engineers who use it. 

It's a very scalable solution and it has all the other big data components. 

The solution is scalable.