The initial setup was seamless because we worked with a gold star Microsoft partner. Our side of setup was pretty quiet. We talked with our partner and told them what we needed from a security and monitoring point of view. We had a few high-level discussions from the block diagram perspective. Basically we said we need this or that, and our partner made it happen. 

The team included one person from our partner and three in-house team members with varying expertise across data modeling, security, and devops. We always worked with the same person but maybe behind the scenes he talked with coworkers. He did talk several times with Microsoft but we don't really know how many people were involved.

The solution does not require infrastructure maintenance. If we ever have issues, we can use Azure Defender to resolve them. We only make slight changes at the application level. 

We have an internal cloud infrastructure team. Our data engineering team is quite large, around 30 people, since multiple projects are happening. Within my immediate team, there are seven people.

The maintenance is primarily handled by our database administrators (DBAs). Our involvement is mainly focused on building data pipelines and the ETL process as part of the data engineering team.

We sometimes need to look into performance issues due to it being in the cloud. However, Snowflake is much easier to maintain – that's all handled by Snowflake itself. Overall, Informatica and Snowflake are less demanding in terms of maintenance compared to ADF.

The implementation was completed in-house and is immediate because it is a native cloud tool.

We have approximately six to seven data engineers from various departments utilizing the solution in our organization. As for the frequency of using GitLab, it varies depending on the workload and projects, but on average, there is someone working on the platform at least several times a week, as our data engineers are involved in various tasks beyond GitLab.

We outsourced the implementation of Azure Data Factory directly to the Azure team.

The implementation was done in-house.

A team of three people deployed Azure Data Factory in three to four days.

I deployed it myself and am also responsible for maintenance.

I did this in-house with the assistance of another team who uses DevOps with Azure for other purposes (non-ADF use). 

We have an entire team that takes care of the cloud infrastructure. So we raise a ticket when we need infrastructure, which is executed based on the naming convention for the project name.

We completed the deployment ourselves.

I implemented through an in-house team. 

We deployed in-house. For deployment, we use ARM templates that are a part of Azure's deployment strategy. It's not only available for Data Factory, it is built in. It links with DevOps, then the ICD integration.

The implementation was completed in-house.

Deployment time is contingent on the volume of data. If there are millions of records to process, it will understandably take a significant amount of time. Conversely, for smaller datasets, the deployment can be relatively quick, often within a matter of minutes to reach the destination. In the cloud deployment process, the initial step involves defining the instance. Subsequently, in the copy activity, we specify both the source and destination. Following this, communication with the destination takes place. This process constitutes the necessary steps for deployment, and we proceed accordingly. Due to the cloud system being provided by Microsoft, they handle the maintenance of the servers.

The solution's implementation process was done with our in-house team's help.

Two of our in-house developers were able to complete the setup.

We have an accelerated solution around it. We utilize our accelerated solutions to spin all these services into the cloud. So, for us, it does not take much time.

An in-house team, the digital data engineering team, deployed Azure Data Factory.

We normally use one or two people to update and maintain Azure Data Factory.

I had to hire technical experts who could help us in the process. We could not handle the implementation ourselves.

We used consultants to implement Data Factory.

We use data engineers to do the install.

We have a team that's in charge of doing the deployments in Azure in different environments.

We do provide the implementation for our clients. We're able to provide templates as well. We have predefined implementation space in Data Factory and provide it to the customer.

We do everything in-house.

We are an enterprise that uses an integrator.

It does not require any maintenance, it's simple.

We implemented the product using both in-house staff, and members of a vendor team. The vendor team were very helpful, and gave good advice while we were deploying the solution.

We implemented the solution in-house. 

You can do deployment in-house. 

We had a discussion with the Microsoft team about the data. We discussed how we were going to implement. Based on the discussion we were able to deploy. A Microsoft partner helped us with some parts. 

We received help from one of the Microsoft partners, who handled the installation for us.

We have a department of developers that implemented the product. The deployment happened before I joined the organization.  

We used a consultant as it was a big project. We had five to six specialists, including both internal and external employees, working on it. It took about about three to six months to complete.

We deployed this solution with our in-house team.

Our deployment was completed by an in-house team.

No maintenance is required and when we experience problems, we normally solve them on our own.

We are a technical team so we implemented it in-house.

The deployment is done in-house with a technical person with knowledge of the Azure cloud.

We first implemented it ourselves but we're using a partner now. We thought we could do it ourselves but we needed specialty help.

I'm an integrator, so I work with clients to help them implement the solution.