Scalability depends on how many job servers you have. For example, if you have one job server and you are trying to process 2 million, 3 million, or 1 billion records, it might take more time. If you have more job servers so that you can run these jobs in parallel, your jobs will run faster. Networking also comes into play. For example, I am in California, and if I am trying to access something in North Carolina and process data, it could be slow. If my server is located in California, it would be pretty fast.

In terms of the number of users, DQ is specific to the data governance team that has five to seven people. For the Talend solution as a whole, we have around 150 people. It is a big solution, but its maintenance is not that big effort because you are not writing any code. If you know Talend and a little bit of Java, managing it should not be a that big effort.

We haven't applied scalability to any existing customer implementations so far.

I would rate the scalability an eight out of ten.

We have not worked extensively on this tool to comment on its scalability.

We have not had any challenges, so I would say that it's a scalable solution.

One of our clients has ten developers. The licensing is based on developers and ten is a large number to have. It's a sizable project.

It's being used on a daily basis by a global client. They don't have data stewards that are available across different countries on the globe for this organization. They access it with the master record that has been created.

They currently have ten developers but plan to increase up to 25 users in the future.

All my projects have been relatively small. I have never needed to scale.

No issues. Only issues were with the Java memory which is scalable and changeable from the job settings.

No issues

The performance is one area that Talend Data Quality could improve in because large volumes take a lot of time.

I encountered scalability issues.

No.