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Copy SQL Server Data in Azure Data Factory

This post is part 16 of 25 in the series Beginner's Guide to Azure Data Factory

In the previous post, we looked at the three different types of integration runtimes. In this post, we will first create a self-hosted integration runtime. Then, we will create a new linked service and dataset using the self-hosted integration runtime. Finally, we will look at some common techniques and design patterns for copying data from and into an on-premises SQL Server.

And when I say “on-premises”, I really mean “in a private network”. It can either be a SQL Server on-premises on a physical server, or “on-premises” in a virtual machine.

Or, in my case, “on-premises” means a SQL Server 2019 instance running on Linux in a Docker container on my laptop 🤓

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Integration Runtimes in Azure Data Factory

This post is part 15 of 25 in the series Beginner's Guide to Azure Data Factory

So far in this series, we have only worked with cloud data stores. But what if we need to work with on-premises data stores? After all, Azure Data Factory is a hybrid data integration service :) To do that, we need to create and configure a self-hosted integration runtime. But before we do that, let’s look at the different types of integration runtimes!

Integration Runtimes

An integration runtime (IR) specifies the compute infrastructure an activity runs on or gets dispatched from. It has access to resources in either public networks, or in public and private networks.

Or, in Cathrine-speak, using less precise words: An integration runtime specifies what kind of hardware is used to execute activities, where this hardware is physically located, who owns and maintains the hardware, and which data stores and services the hardware can connect to.

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Annotations and User Properties in Azure Data Factory

This post is part 14 of 25 in the series Beginner's Guide to Azure Data Factory

In the previous post, we looked at how monitoring and alerting works. But what if we want to customize the monitoring views even further? There are a few ways to do that in Azure Data Factory. In this post, we will add both annotations and custom properties.

But before we do that, let’s look at a few more ways to customize the monitoring views.

Customizing Monitoring Views

In the previous post, we mainly looked at how to configure the monitoring and alerting features. We saw that we could change filters and switch between list and Gantt views, but it’s possible to tweak the interface even more to our liking.

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Monitoring Azure Data Factory

This post is part 13 of 25 in the series Beginner's Guide to Azure Data Factory

In the previous post, we looked at the three different trigger types, as well as how to trigger pipelines on-demand. In this post, we will look at what happens after that. How does monitoring work in Azure Data Factory?

Now, if we want to look at monitoring, we probably need something to monitor first. I mean, I could show you a blank dashboard, but I kind of already did that, and that wasn’t really interesting at all 🤔 So! In the previous post, I created a schedule trigger that runs hourly, added it to my orchestration pipeline, and published it.

Let’s take a look at what has happened since then!

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Triggers in Azure Data Factory

This post is part 12 of 25 in the series Beginner's Guide to Azure Data Factory

In the previous post, we looked at testing and debugging pipelines. But how do you schedule your pipelines to run automatically? In this post, we will look at the different types of triggers in Azure Data Factory.

Let’s start by looking at the user interface, and dig into the details of the different trigger types.

Creating Triggers

First, click Triggers. Then, on the linked services tab, click New:

Screenshot of Azure Data Factory user interface with Triggers open, highlighting the button for creating a new trigger

The New Trigger pane will open. The default trigger type is Schedule, but you can also choose Tumbling Window and Event:

Screenshot of Azure Data Factory user interface with the New Trigger pane open and the different trigger types highlighted

Let’s look at each of these trigger types and their properties :)

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