Welcome to this Beginner’s Guide to Azure Data Factory! In this series, I’m going to cover the fundamentals of Azure Data Factory in casual, bite-sized blog posts that you can read through at your own pace and reference later. You may not be new to ETL, data integration, Azure, or SQL, but we’re going to start completely from scratch when it comes to Azure Data Factory.
How do you get started building data pipelines? What if you need to transform or re-shape data? How do you schedule and monitor your data pipelines? Can you make your solution dynamic and reusable? Join me in this Beginner’s Guide to Azure Data Factory to learn all of these things - and maybe more. 🤓 Let’s go!
P.S. This series will always be a work-in-progress. Yes, always. Azure changes often, so I keep coming back to tweak, update, and improve content. I just might not be able to do it right away!
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 😎
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? 😂
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.
Pssst! The debugging experience has had a huge makeover since I first wrote this post. I'm working on updating the descriptions and screenshots, thank you for your understanding and patience 😊
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.
Pssst! Triggers 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 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 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.
