In the previous post, we looked at the copy data activity and saw how the source and sink properties changed with the datasets used. In this post, we will take a closer look at some common datasets and their properties.
Let’s start with the source and sink datasets we created in the copy data wizard!
First, a quick note. If you use the copy data wizard, you can change the dataset names by clicking the edit button on the summary page…
In the previous post, we went through Azure Data Factory pipelines in more detail. In this post, we will dig into the copy data activity. How does it work? How do you configure the settings? And how can you optimize performance while keeping costs down?
Copy Data Activity
The copy data activity is the core (*) activity in Azure Data Factory.
(* Cathrine’s opinion 🤓)
You can copy data to and from more than 80 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). During copying, you can define and map columns implicitly or explicitly, convert file formats, and even zip and unzip files – all in one task.
Yeah. It’s powerful :) But how does it really work?
In the previous post, we used the Copy Data Wizard to copy a file from our demo dataset to our storage account. The Copy Data Wizard created all the factory resources for us: pipelines, activities, datasets, and linked services.
In this post, we will go through pipelines in more detail. How do we create and organize them? What are their main properties? Can we edit them without using the graphical user interface?
Pipelines: The Basics
When I was new to Azure Data Factory, I had many questions, but I didn’t always have someone to ask and learn from. When I did work in a team, I didn’t always dare ask my team members for help, because I felt silly for asking about things that I felt I should probably know.
Yeah, I know… It’s easy to tell others that there are no silly questions, but I don’t always listen to myself :)
I don’t want you to feel the same way! So. Let’s start from the beginning. These are the questions that I had when I was new to Azure Data Factory. Or, these are the questions that I realized I should have asked when I discovered something by accident and went “Oh! So that’s what that is! I wish I knew that last week!“
In the previous post, we looked at the different Azure Data Factory components. In this post, we’re going to tie everything together and start making things happen. Woohoo! First, we will get familiar with our demo datasets. Then, we will create our Azure Storage Accounts that we will copy data into. Finally, we will start copying data using the Copy Data Wizard.
First, let’s get familiar with the demo datasets we will be using. I don’t know about you, but I’m a teeny tiny bit tired of the AdventureWorks demos. (I don’t even own a bike…) WideWorldImporters is at least a little more interesting. (Yay, IT joke mugs and chocolate frogs!) But! Let’s use something that’s not already in relational database format.
In the previous post, we started by creating an Azure Data Factory, then we looked at the user interface and the three main Azure Data Factory pages. In this post, we will go through the Author page in more detail. Let’s look at the different Azure Data Factory components!
Azure Data Factory Components
On the left side of the Author page, you will see your factory resources. In this example, we have already created one pipeline, two datasets, and one data flow:
Let’s go through each of these Azure Data Factory components and explain what they are and what they do.