The initial setup was quite quick. Deployment was fairly simple and took less than a week. The setup got us up and running. 

After that, we had to write the implications of the data model and the kinds of activities. We are still doing this today because we make changes all the time.

The initial setup of Azure Data Factory is not complex if you know what you are doing. If you do not know the technology you will have a problem.

That's likely handled by your cloud infrastructure team.

The initial setup is straightforward.

The deployment was handled in-house, and initially, we had a team of five working on the project in 2019. 

I rate Azure Data Factory eight out of 10 for ease of setup. The deployment time depends on the data volume. Four million records will take longer than four thousand. Migrating our full load from on-prem to the cloud took around 16-18 hours because the volume was 17 million. 

My company didn't set up Azure Data Factory as the Azure team did it.

The initial setup is straightforward.

You need to be experienced in deploying the solution. It's not so easy for a business user. Depending on the use case, it takes around six months to get a proof of concept done.

I rate the initial setup a seven out of ten, where one is easy and ten is difficult.

On a scale from one to ten, where one is difficult and ten is easy, I rate the solution's initial setup a seven out of ten.

The initial setup is pretty simple and it can be deployed in a couple of hours.

The initial setup for Azure Data Factory is an eight out of ten.

Manually deploying Azure Data Factory is easy and doesn't take much time, but I'm not sure how long it takes for an automated approach to deployment.

Initial setup is the largest caveat for this tool. Once you've organized your Azure environment and set up DevOps pipelines, the rest is a breeze. But this is NOT a trivial step if you're the first one to establish the use of ADF at your organization or within your subscription(s). Instead of learning just an ETL tool, you have to get familiar with and establish best practices for the entire Azure and DevOps technologies. That's a lot to take on just to get some data movements operational. 

It does not require any installation and is more like software as a service. You need to create an instance of Azure Data Factory in Azure and configure some of the connections to your databases. You can connect to your block storages and some authentication is necessary for Azure Data Factory.

The setup is straightforward. It doesn't take much time, and it's on cloud. It requires a few clicks, and you can quickly set it up and grant access to the developer. Then the developer can go to the link and start developing within their browser.

We have a team that takes care of the cloud infrastructure, so we raise a ticket and request infrastructure, and they just exceed it based on the naming convention with the project name.

The initial setup is straightforward.

It is a working tool. You can start using it within an hour and then make changes as needed.

We only need one person to maintain the solution; it doesn't take much to keep it running.

It's not a problem; it's a platform.

There is no installation because it's on the cloud. You just log on to the cloud with your subscription credentials, then you can use Data Factory directly. 

The solution is deployed on the cloud.

All we needed to do was create ARM templates and deployment is easy.

The initial setup is complex.

The tool's deployment is easy. The deployment typically takes around two to three days to set up. However, the duration may vary depending on factors such as the number of integrated endpoints. In our company, the deployment team had three to four people. This team consisted of an IT engineer, a network engineer, and an ETL admin.

We still haven't required much maintenance since we're still in the development phase. However, as time progresses and we move into production, we'll better understand the maintenance requirements.

The initial setup is straightforward and the team is able to deploy between six and seven days.

The initial setup has been smooth and without any difficulties.

The solution's initial setup process was easy. On a scale where one is difficult and ten is easy, I rate the initial setup process an eight out of ten. The solution is deployed on the cloud.

Since multiple projects are going on in my organization, there is no uniformity in the time taken to deploy the solution in our company. However, I can say that it only takes a few days to carry out the deployment process.

Our organization has multiple project teams, so each team carries out its deployment process.

To give an average, I would consider that if there are fifty ongoing projects in our company, and if we consider a person from each project, fifty people are needed for the deployment and maintenance process.

The initial setup was quick and easy. The whole process took about fifteen minutes. We have about a hundred users at the moment and have plans to increase.

I rate the product's initial setup phase a seven or eight on a scale of one to ten, where one is difficult and ten is easy.

In my company, we take care of the product's deployment process and maintenance phase.

The solution is deployed using Azure's cloud services.

The solution can be deployed in ten to fifteen minutes.

For deployments, my company usually creates codes in Terraform so that we can have automated deployments, and it is connected to us with a CI/CD tool like Azure DevOps. Azure DevOps does the automated deployment for our company.

During the setup phase, users may face issues when it comes to infrastructure deployment and the configuration around it, especially if you consider the integration runtime, as it is something that is too complicated for a normal developer to understand. There is a need for a cloud expert with a good understanding to be able to take care of the deployment in the right manner and in a secure way. The networking setup and security part of the product are a bit complicated, which I might understand since I am a DevOps engineer, but a developer who is new to the product might not understand such parts of the tool. The deployment of the service in an infrastructure can be possible only if the person involved in the deployment has a basic level of understanding related to the product.

The initial setup was straightforward, and only one person was required for deployment.

Data Factory is effortless to set up.

It was of medium complexity. Because it goes to the cloud, the duration was short. The deployment was minutes and hours.

From a cloud perspective, the initial setup is straightforward. We have a data engineering team with about 15 professionals managing and maintaining this solution.

Azure Data Factory's initial setup needs some amount of professional training and qualification.

The initial setup for Azure Data Factory was easy, so I'd rate the setup a four out of five.

The implementation strategy was looking into what my organization needed overall, then planning and direct deployment. My company first did a test, a dummy version, then a UAT with stakeholders before going into production.

It took about two months to complete the deployment for Azure Data Factory.

The initial setup is of a moderate level of difficulty. However, it can be complex. The solution is able to fit both of our use cases.

The initial setup was complex, but the deployment only took 30 to 40 minutes.

The initial setup is not straightforward. It's a bit complex. A company may need to hire someone to assist them with the process.

The solution's deployment took about eight weeks.

The initial setup was complex. The deployment was carried out in-house and we had around 10 people involved in the implementation. 

The initial setup is easy.

It took three to four weeks to get up to speed and get comfortable using it.

The initial setup is straightforward. Deployment is automatic and takes few minutes. 

The initial setup is complex because there are a lot of prerequisites, including plumbing in the network, but that's typical for any cloud-based solution.

The installation is straightforward.

The initial setup is straightforward. I think that it's so easy to start a project using that technology.

We were not actually involved in the initial setup. That was all with the client, so I won't be able to comment on it.

The initial setup is relatively straightforward. That's assuming that when you're creating the Data Factory, you have some knowledge about how to create it. We deployed in-house and it took about ten minutes for the initial creation process. The provisioning itself is not something you have control over because it's a self-service that does what it needs and happens in the background. The infrastructure doesn't require any maintenance, it's all managed on the backend. There is maintenance in terms of your codes, connections, and the like, but that is separate from the infrastructure maintenance.

Setting up Azure Data Factory is pretty straightforward. We had an Azure account already, and Data Factory was just something we could add as an extra service. We had to create instances and pipelines, and it took us about two weeks to get our first pipelines scheduled and running. 

It’s hard to describe the installation. It’s not overly complex or extremely easy.

The point is almost true for all services. If you want to do something simple and quick, then it's just a couple of clicks, and it's there. However, in real production environments, it's not like that. You have to arrange a lot of things. You have to set up a lot of things. You have to configure a lot of things correctly in an automated way. That is totally different than just a couple of clicks. You have to put in the work. If you ask me how easy it is, yeah, it is easy. However, it can also be really, really complicated depending on the scenario.

I don't think that the initial setup was very complex. We have quite an advanced IT infrastructure team and the Microsoft FastTrack team also helped us a lot during the programming of the development and setup.

I was not involved with the initial setup.

It is pretty straightforward. Devil is in the details, but you can easily get up and running in a day with Data Factory. Anybody who is comfortable in Azure can set up Data Factory, but it takes experience to know what it can and can't do or should and shouldn't do.

The initial setup of this product is straightforward, if you deploy the solution using a template; rather than implementing the solution first, and configuring the features afterwards.

The setup is not very complex but requires intake, setting up integration services, and connecting to databases like Oracle before you push it to service.

The setup is complex. It has nothing to do with the technology but with the design. We were wondering how to leverage the orchestration layer where we are having the Azure Data Factory and how to integrate with the Databricks. That's where we had some challenges in terms of choosing the right product.

Since the solution is a service, it's basically just a click and run setup. It's very simple. There's very little implementation necessary. A company should be able to easily arrange it. The deployment doesn't take very long at all.

The initial setup was a bit complex but once you understand its setup, it's less complex. There are certain processes that need to be followed. Once you understand the process, it becomes easier to implement.

The implementation took a little less than one day. The planning for the deployment takes around one or two days. 

The initial setup was easy as it's a SaaS offering.

The initial setup is not very straightforward. I rate it a seven out of ten. 

The initial setup was not very complex and the deployment was very quick. We were set up within a matter of days, or perhaps a week.

The initial setup was not simple and it was not complex. It was in the middle.  

I would say it took two months for the deployment.  

The initial setup was straightforward. It was not complex.

The initial setup is straightforward. I'm a huge fan and user of CI/CD pipelines and never do deployments manually. It's all automated and deployment takes a few minutes.

The initial setup is very easy.

The initial setup of the Azure Data Factory was simple. It took us one week to deploy it.

Setup is very straightforward and simple, but it depends on the customers. We have more than 15 data engineers who have data engineer certification from Microsoft. For them, it's a very easy process. 

Of course, there are some issues when configuring the on-premise integration runtime because you have to deal with the network settings on on-premise infrastructure. In terms of ADF as a product, there are very good guides and documentation that you can use to navigate how to solve certain issues.

The initial setup is pretty straightforward. However, there is a bit of a learning curve, but once you get it, it's pretty simple.

The initial setup is simple and it only takes perhaps five minutes to deploy.

When one of our developers wants to use it, they can create their own jobs.

The initial setup was very straightforward. I wouldn't describe it as complex.

Deployment is fast. It only takes about half an hour at most.

The initial setup was very straightforward.

The initial setup was quite quick, nothing much to do. Now, we are more developing the use cases. A use case with data generally takes around four or five days a use case because it will start right from identifying the right field, getting the data, transforming it, and finalizing the warehouse structure. That makes a bit of a thing, but it's pretty straightforward.

The installation is very simple and it doesn't take much time. For us, the deployment took about two days, which does not seem unreasonable for something that is on the cloud. Most of the time is spent waiting for credentials.

Depending on the sources of the data, four people are required for deployment and maintenance. If the sources are SQL databases then it is straightforward and four people can cope with it. If the data is more difficult then we may need more people.

The initial setup is complex.

It was very easy.

The initial setup was straightforward.

We didn't have to deploy Azure Data Factory. It's available as an Azure service, so Microsoft takes care of that.

The initial setup is very straightforward. The only thing which we had to consider at the beginning was the gateway for the on-premises environment. Otherwise, it's very easy.

The initial setup is straightforward. It's got a very nice welcome page where everything explained. Depending on the complexity, deployment takes only a few days. You only need one person for the deployment of the solution. This is dependent, however, on how many pipelines you are implementing. Afterward, you would probably only need one person to maintain it.

The initial setup was pretty fast and simple. I wouldn't say it was too complex. For deployment, it depends on your environment. In our POC, it didn't take more than two to three hours. In the production environment, however, it takes a bit more time for operations.