The solution is scalable with no performance issues. We haven't yet reached our limit that would require scaling. Scalability is rated an eight out of ten. 

Azure Data Factory is scalable. The solution can move up and be aligned to resources or scaled down.

We have a lot of customers using the solution, approximately 100.

When it comes to scalability and handling large datasets, it works well for datasets within the Azure environment because it's tightly integrated. 

However, for third-party integrations – things like SAP HANA, MongoDB, or handling semi-structured and unstructured data from logs – it's not as reliable. ADF excels with tight Azure cloud integration.

There are around seven end-users. Across the enterprise, Informatica is our main tool because it includes data governance (DG/DM), data quality (DQ), data cataloging, API integration, and streaming capabilities – like IDM and Informatica Cloud Services (ICS) as a SaaS platform hosted on either AWS or Azure. 

We are transitioning SSIS pipelines to ADF, but otherwise, Informatica is our central tool.

I would rate the scalability an eight out of ten.

The solution is scalable.

In terms of scalability, there are a few aspects of GitLab that I find disappointing. For instance, the limitation on self-hosted integration run time to just four VMs restricts scalability, especially for handling large volumes of data. Improvements are needed in this area to support more than four VMs for scalability. The documentation regarding bandwidth support is unclear, making it difficult to assess the full scalability potential. While GitLab performs well in cloud scalability in terms of compute power, the limitations on self-hosted integration run time are a concern for certain use cases. scalability in GitLab is highly dependent on specific use cases and could benefit from enhancements in self-hosted integration run time capabilities.

Azure Data Factory is scalable, with clusters available on demand. There isn't any issue with scaling the solution.

The solution is scalable and we intend to further increase its usage in the future.

I rate the solution’s scalability an eight out of ten.

Azure Data Factory is a scalable solution. A team of 16 people from the data analytics team use the solution in our organization.

I rate the solution an eight out of ten for scalability.

I have not seen any issues with respect to scalability, as it is all hosted within the cloud. We have approximately 20 users.

Azure Data Factory is a scalable tool.

So far, the performance of this solution is abysmal compared to SSIS. Especially with small tasks such as copying activity from one table to another within the same database. 

In regards to scaling, when Azure Data Factory was introduced as your Databricks, it worked similarly to Hadoop or Spark, and it had some Spark clusters in the back end that could scale it as much as it could, and speed up the performance. So it is scalable, especially with Databricks, because a lot of data-related transformations can be performed.

On my team, there are approximately 20 people who work with Azure Data Factory.

There is no limit to scalability.

We only have a few users. One is a data scientist, and the other is a data analyst.

We use it to push up various dashboards and reports, it's a transitional product for transferring, transforming, and transitioning data.

It is extensively used, and we intend to expand our use.

This solution is automatically scalable, since it's in the cloud. At my company, there were more than one thousand people using this solution because we were a big, media-based company. If there are many user requests in the front end application and the system is not responding much or has slow performance, the system will automatically scale up the performance hardware requirements. 

Scalability depends on the use case. 

I rate the tool's scalability an eight out of ten. 

The solution is scalable.

It provides impressive scalability. There are a total of eight switches currently in use. I would rate it nine out of ten.

Scalability-wise, I rate the solution a seven or eight out of ten. So, scalability can be improved. Also, there are around 150 people in my company using the solution. Moreover, we use the solution daily in our company.

The solution is scalable.

The scalability of the product is impressive. Scalability-wise, I rate the solution an eight out of ten.

Most of the people in my company work on Azure, and those who want to use the native ETL capabilities provided by the product opt for Azure Data Factory.

The product is useful in medium to large-sized businesses. Smaller businesses can opt for other options other than Azure Data Factory, considering the amount of money they are ready to spend. There are better options available in the market than Azure Data Factory.

I would rate Data Factory's scalability eight out of ten.

It isn't that expensive to scale Data Factory up. My client can ask for more resources on the tool, and paying more is never an issue. 

It is very scalable. It is a cloud product. It is being used by business analysts, business managers, and Azure cloud architects. We have just one developer/integrator for deployment and maintenance purposes.

We have plans to increase its usage. We'll be rolling it out for other clients.

Azure Data Factory is a scalable solution. We have around 15 to 20 customers, of which about 8 to 10 use Azure Data Factory.

Scalability-wise, Azure Data Factory is a four out of five because Microsoft is still developing certain tiers, which means you can't upgrade an older skill or tier. In contrast, the more modern, newer tiers could be upgraded easily. Rarely will you get stuck in one platform where you have completely destroyed that container and then fit a new container. Most of the time, Azure Data Factory is pretty easy to scale.

There is a team of 30 people working on the solution. 

The solution is highly scalable.

This solution is scalable.

The solution is pretty easy to scale on Azure. I have found it to be very efficient and it is pretty fast. You just need to get the order done properly, and then you will be able to scale up.

We have about five to seven people using it at this time.

There are thousands of users so the solution is scalable. 

We've had no problems with Data Factory's scalability.

Azure Data Factory is a scalable product.

In my current company, I have a team of five people, but in my previous organization, there were 20.

It is easy to scale. 

Data Factory is scalable.

Yes, Azure Data Factory is scalable.

It's scalable. We're doing a lot of different integrations with a lot of data, and scalability is great.

The solution allows you to create reusable components, so it can be scaled pretty easily.

The way we've used the product is around streaming data, and that doesn't work well with Data Factory. As loads increase, some of the underlying infrastructure that gets used to process data seems to slow down. This is basically a development product, so in terms of scalability it doesn't have a wide user base, it's only meant for developers and analysts. The number of users will vary from anywhere between one to five people. 

It's a cloud solution, so it's inherently scalable. I don't know If we have to raise the limits on resources like clusters and processing power or if it will just automatically scale up. I can't remember offhand. 

For Azure Data Factory, scalability doesn't mean really too much. However, in some scenarios, you can play with it a little bit.

Azure Data Factory is not for users. Is for engineers, for developers. The end user does not interact with Azure Data Factory. There might be 20 developers on the solution currently.

Scalability is one of the points that we were looking for because we are hosting approximately two terabytes of data and we expect that it will grow at least five times over the next two years. This is one of the reasons that we adopted this solution.

In perhaps a year, we will increase our usage.

I haven't had to scale this solution as of yet.

We have six people in our company who use this solution.

Increasing the usage is not on our strategy pathway.

It is decent for most things. I'm not sure if it is necessarily intended for large volume and high-speed streams of data. By large, I mean really big, but for pretty much anything that most users would want to do, including ourselves, it is fine. Our clients are large government organizations.

It scales fine within its environment. You can literally throw another Data Factory in or replicate one and do things pretty quickly. So, it is not at all hard to increase your processing footprint, but you have to pay for it. It doesn't end up being quite expensive. Although I haven't really done it, I would suspect that if I did the equivalent in AWS, Azure would be more expensive than AWS because of the way they price data.

Data Factory is scalable.

This is an easily scalable product, due to it being cloud-based.

The solution is scalable. 

For multi-tenant applications connected to multiple databases, Microsoft recommends a share box and a cell post integration run time. But a run time connecting to multiple sources has limitations and requires multiple shares connecting to your data if you are ingesting it from on-premises. 

It's scalable.

We work with medium to enterprise-level organizations. Customers have anywhere from 300 employees up to 160,000 employees.

I haven't had much experience with scalability. I know we do have scalability options though. It's used daily. 

There are around 1,000 plus users using this solution in my company. 

It requires two people for maintenance. The administrators are the ones who maintain it and give access to the engineers. They regulate who has privileges. 

Data Factory's scalability is good.

6 developers are using the solution at present. 

The scalability seems okay. As we have only been using it for a short time, it is hard to say more. We are not currently planning to scale usage dramatically at this point but of course we would like to grow. On a scale from one to ten and from what I know, I would say scalability is an eight-out-of-ten. I can't be sure exactly how many people are using the system, but we have hundreds of thousands of users currently. Internally, I would say we use the product often.  

I haven't put much thought into scalability.

The solution is easy to scale keeping in mind that Data Factory doesn't do any computations. We use it mainly to push the computations to Databricks or Synapse. Projects with our clients generally last a few months and only until they go into production. I believe the ability to increase is always there.

Azure Data Factory is a very scalable solution. Including internal developers and external consultants working for us, we have about 10-15 people using this solution.

The scalability is good. We have about five users for this solution and they are all developers.

It's easy to scale.

Azure Data Factory is extremely simple to scale.

This solution is used by a dozen people in our company.

Being a cloud product, it can scale as much as we need.

In my project, there are ten people using this product.

When it comes to scalability, the solution is similar to the previous Azure Synapse Analytics. They are both very easy.

I haven't had the ability to scale any of my projects personally. I also wouldn't need to scale too high if I did. I'm not sure if I can speak to aspects of scalability as I've never dealt with it.

We have not tried it scaling up. But, Azure promises the stability and scalability should not be an issue.

From a development perspective, I think there were four developers who use Azure Data Factory. From a warehouse perspective, once we roll out the reports out, it should be used by at least 40 or 50 people minimum.

This solution is 100% scalable.

We have two clients working with this solution.

Azure Data Factory is a scalable solution.

It is scalable. From the security perspective, it depends on how we implement it. It depends on the organization, but I don't see a challenge. 

In my previous organization, we had close to a hundred people.

We don't have any complaints regarding scalability or stability.

The solution is scalable.

The scalability of the connected engines makes this solution very scalable.

The solution is scalable. Right now, we have only three or four people on it. We may increase usage int eh future.