Cathrine Wilhelmsen continues a series on Azure Data Factory. Since we left off, Cathrine has three new posts. First, a look at how we monitor Azure Data Factory pipelines:
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.
Annotations are additional, informative tags that you can add to specific factory resources: pipelines, datasets, linked services, and triggers. By adding annotations, you can easily filter and search for specific factory resources.
You need to figure out what kind of annotations make sense to you. Maybe you want to filter on the different logical steps of your solution, so you add the tags extract and transform? Perhaps ingest and prepare? Or maybe you want to tag resources with a business unit or a project name? It’s entirely up to you. All I recommend is that you’re consistent 🙂
That’s a problem for me—the only thing I’m consistent about is inconsistency. Third, Cathrine introduces the different runtimes available to us:
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.
There’s a lot of good material in each of these three posts.