Raise your hand if you have wondered why you can only publish and not save anything in Azure Data Factory 🙋🏼♀️ Wouldn’t it be nice if you could save work in progress? Well, you can. You just need to set up source control first! In this post, we will look at why you should use source control, how to set it up, and how to use it inside Azure Data Factory.
And yeah, I usually recommend that you set up source control early in your project, and not on day 19… However, it does require some external configuration, and in this series I wanted to get through the Azure Data Factory basics first. But by now, you should know enough to decide whether or not to commit to Azure Data Factory as your data integration tool of choice.
Get it? Commit to Azure Data Factory? Source Control? Commit? 🤓
Ok, that was terrible, I know. But hey, I’ve been writing these posts for 18 days straight now, let me have a few minutes of fun with Wil Wheaton 😂
Two posts ago, we looked at the three types of integration runtimes and created an Azure integration runtime. In the previous post, we created a self-hosted integration runtime for copying SQL Server data. In this post, we will complete the integration runtime part of the series. We will look at what SSIS Lift and Shift is, how to create an Azure-SSIS integration runtime, and how you can start executing SSIS packages in Azure Data Factory.
(And if you don’t work with SSIS, today is an excellent day to take a break from this series. Go do something fun! Like eat some ice cream. I’m totally going to eat ice cream after publishing this post 🍦)
Pssst! Integration Runtimes have been moved into the management page. I'm working on updating the descriptions and screenshots, thank you for your understanding and patience 😊
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.
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!
Pssst! Integration Runtimes have been moved into the management page. I'm working on updating the descriptions and screenshots, thank you for your understanding and patience 😊
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.