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Debugging Pipelines in Azure Data Factory

This post is part 11 of 25 in the series Beginner's Guide to Azure Data Factory

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:

Screenshot of the Azure Data Factory interface, with a pipeline open, and the debug button highlighted

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.

Debugging Pipelines

Let’s start with the most important thing:

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Orchestrating Pipelines in Azure Data Factory

This post is part 10 of 25 in the series Beginner's Guide to Azure Data Factory

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?

Diagram showing data being copied from an on-premises data center to Azure Data Lake Storage, and then transformed from Azure Data Lake Storage to Azure Synapse Analytics (previously Azure SQL Data Warehouse)

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

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Data Flows in Azure Data Factory

This post is part 9 of 25 in the series Beginner's Guide to Azure Data Factory

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…

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Linked Services in Azure Data Factory

This post is part 8 of 25 in the series Beginner's Guide to Azure Data Factory

In the previous post, we looked at datasets and their properties. In this post, we will look at linked services in more detail. How do you configure them? What are the authentication options for Azure services? And how do you securely store your credentials?

Let’s start by creating a linked service to an Azure SQL Database. Yep, that linked service you saw screenshots of in the previous post. Mhm, the one I sneakily created already so I could explain using datasets as a bridge to linked services. That one :D

Creating Linked Services

First, click Connections. Then, on the linked services tab, click New:

Screenshot of the Azure Data Factory user interface showing the connections tab with linked services highlighted

The New Linked Service pane will open. The Data Store tab shows all the linked services you can get data from or read data to:

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Datasets in Azure Data Factory

This post is part 7 of 25 in the series Beginner's Guide to Azure Data Factory

In the previous post, we looked at the copy data activity and saw how the source and sink properties changed with the datasets used. In this post, we will take a closer look at some common datasets and their properties.

Let’s start with the source and sink datasets we created in the copy data wizard!

Dataset Names

First, a quick note. If you use the copy data wizard, you can change the dataset names by clicking the edit button on the summary page…

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