Now, we'll create the factory. The goal of the exercise is to copy data from a SQL Server table and bring it in an Azure Blob storage.

Log in to the Azure portal (http://Portal.Azure.com). In the resource section, click the + New icon. Click on Data + Analytics and select Data Factory, as shown in the following screenshot:

The New data factory blade opens. As shown in the following screenshot, fill the textboxes with the following values:

• Name: The name of the factory might be later registered as DNS. It should be unique if at all possible. To make it unique, we might use our initials in front of it. There are naming rules for data factories, which can be found at https://docs.microsoft.com/en-us/azure/data-factory/naming-rules.
• Subscription: Should be filled with the active subscription used to create the data factory.
• Resource Group: We're using the resource group created earlier in this chapter.
• Version: Since this book talks about V2, we'll use V2 of the data factory. At the time of writing, the version was in preview.
• Location: This time, the location is important. We'll choose the one that we used for the resource group; this is also the one that will be closer to our data in Azure to avoid supplemental charges.

Once we've filled out all properties, we can select Pin to dashboard to make our data factory handy. We can click on Create to start building the factory:

The factory gets deployed.

Once the factory is created, its blade opens, as shown in the following screenshot:

When we click on Author & Monitor, the factory's object creation blade opens, as shown in the following screenshot:

We'll spend a lot of time in the factory's object creation blade. Clicking on the pencil icon, shown in the following screenshot, will bring the editor:

As shown in the following screenshot, the editor is empty. We'll add some objects later:

For now, let's talk about the factory pipeline. The pipeline is the heart of the data factory. This is where we define data movements and transformations. There are other types of artifacts related to the pipeline, and we'll talk about them in the next sections: Linked services, Integration runtimes, Datasets, and Activities.