In the previous post, we looked at foreach loops and how to control them using arrays. But you can also control them using more complex objects! In this post, we will look at lookups. How do they work? What can you use them for? And how do you use the output in later activities, like controlling foreach loops?

Lookups

Lookups are similar to copy data activities, except that you only get data from lookups. They have a source dataset, but they do not have a sink dataset. (So, like… half a copy data activity? 😄) Instead of copying data into a destination, you use lookups to get configuration values that you use in later activities.

And how you use the configuration values in later activities depends on whether you choose to get the first row only or all rows.

But before we dig into that, let’s create the configuration datasets!

In the previous post, we looked at how to use variables in pipelines. We took a sneak peek at working with an array, but we didn’t actually do anything with it. But now, we will! In this post, we will look at how to use arrays to control foreach loops.

ForEach Loops

You can use foreach loops to execute the same set of activities or pipelines multiple times, with different values each time. A foreach loop iterates over a collection. That collection can be either an array or a more complex object. Inside the loop, you can reference the current value using @item().

Let’s take a look at how this works in Azure Data Factory!

In the previous post, we talked about why you would want to build a dynamic solution, then looked at how to use parameters. In this post, we will look at variables, how they are different from parameters, and how to use the set variable and append variable activities.

Variables

Parameters are external values passed into pipelines. They can’t be changed inside a pipeline. Variables, on the other hand, are internal values that live inside a pipeline. They can be changed inside that pipeline.

Parameters and variables can be completely separate, or they can work together. For example, you can pass a parameter into a pipeline, and then use that parameter value in a set variable or append variable activity.

In the last mini-series inside the series (🙃), we will go through how to build dynamic pipelines in Azure Data Factory. In this post, we will look at parameters, expressions, and functions. Later, we will look at variables, loops, and lookups. Fun!

But first, let’s take a step back and discuss why we want to build dynamic pipelines at all.

In the previous post, we looked at setting up source control. Once we did that, a new menu popped up under factory resources: templates! In this post, we will take a closer look at this feature. What is the template gallery? How can you create pipelines from templates? And how can you create your own templates?

Let’s hop straight into Azure Data Factory!

Using Templates from the Template Gallery

From the Home page, you can create pipelines from templates: