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!
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.
First, click Triggers. Then, on the linked services tab, click New:
The New Trigger pane will open. The default trigger type is Schedule, but you can also choose Tumbling Window and Event:
Let’s look at each of these trigger types and their properties :)
In the previous post, we looked at orchestrating pipelines using branching, chaining, and the execute pipeline activity. In this post, we will look at debugging pipelines. How do we test our solutions?
You debug a pipeline by clicking the debug button:
Tadaaa! Blog post done? :D
I joke, I joke, I joke. Debugging pipelines is a one-click operation, but there are a few more things to be aware of. In the rest of this post, we will look at what happens when you debug a pipeline, how to see the debugging output, and how to set breakpoints.
In the previous post, we peeked at the two different data flows in Azure Data Factory, then created a basic mapping data flow. In this post, we will look at orchestrating pipelines using branching, chaining, and the execute pipeline activity.
Let’s continue where we left off in the previous post. How do we wire up our solution and make it look something like this?
We need to make sure that we get the data before we can transform that data.
One way to build this solution is to create a single pipeline with a copy data activity followed by a data flow activity. But! Since we have already created two separate pipelines, and this post is about orchestrating pipelines, let’s go with the second option :D
So far in this Azure Data Factory series, we have looked at copying data. We have created pipelines, copy data activities, datasets, and linked services. In this post, we will peek at the second part of the data integration story: using data flows for transforming data.
But first, I need to make a confession. And it’s slightly embarrassing…