Hover over the mapping, and click the delete button that appears next to it: You can also remove mappings, for example if you are copying data to a text file and only want to keep a subset of the columns from the source. You have to do this if the source and sink are different: If you leave the mappings empty, Azure Data Factory will do its best to map columns by column names:Įxplicit mapping is when you decide how to map columns from the source to the sink. In the copy data activity, you can map columns from the source to the sink implicitly or explicitly. Then go click and type things in the copy data activity until you configure it the way you need to for your solution. When you use a new dataset or data store, reference that overview, and read up on the details. So! Here’s what I do, and what I recommend you do: Bookmark the Azure Data Factory connector overview. More importantly, you don’t want me trying to explain ALL THE THINGS. Here’s an example of four different datasets used as a source:Īnd here’s an example of four different datasets used as a sink:Īnd with over 90 connectors… phew! I don’t even know the details of all the connectors. Why? Because the properties completely depend on the type of dataset and data store you are using. But it’s nearly impossible for me to go through the properties in detail. It’s where you specify the datasets you want to copy data from and to. It’s easy to explain the concept of the source and sink properties. It may be worth peeking at the code when learning Azure Data Factory to understand things even better. However, I wanted to show you an example of things you can find and discover in the JSON code that might not be obvious from the graphical user interface. The fact that they are called policies isn’t really important. Here, you can see that these properties are grouped under “ policy”: This opens up the JSON code view for the activity. Yep, let me show you! Click the view source code button on the copy data activity: These policies are available for the execution activities, meaning most activities except the control activities. ( You probably don’t want to log anything containing sensitive data.) You can hover the little information icons for more details: In addition to the name and description, you can change the copy data activity policies for activity timeout, retry attempts, retry interval, and whether or not to log input and output details. The copy data activity properties are divided into six parts: General, Source, Sink, Mapping, Settings, and User Properties.įirst things first! Let’s change the name into something more descriptive than the random “Copy_09c” that was auto-generated in the copy data tool:Īaaaah, that’s better! Now I can focus □ Alright. It’s powerful! But how does it really work? During copying, you can define and map columns implicitly or explicitly, convert file formats, and even zip and unzip files – all in one task. You can copy data to and from more than 90 Software-as-a-Service (SaaS) applications ( such as Dynamics 365 and Salesforce), on-premises data stores ( such as SQL Server and Oracle), and cloud data stores ( such as Azure SQL Database and Amazon S3). The copy data activity is the core ( *) activity in Azure Data Factory. How does it work? How do you configure the settings? And how can you optimize performance while keeping costs down? Copy Data Activity In this post, we will dig into the copy data activity. In the previous post, we went through Azure Data Factory pipelines in more detail. Post 7 of 26 in Beginner's Guide to Azure Data Factory
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