Category: Cloud

Paul Andrew has a nice set of recommendations you should follow when configuring Azure Data Factory:

Building on our understanding of generic datasets, a good Data Factory should include (where possible) generic pipelines, these are driven from metadata to simplify (as a minimum) data ingestion operations. Typically I use an Azure SQLDB to house my metadata with stored procedures that get called via Lookup activities to return everything a pipeline needs to know.

This metadata driven approach means deployments to Data Factory for new data sources are greatly reduced and only adding new values to a database table is required. The pipeline itself doesn’t need to be complicated. Copying CSV files from a local file server to Data Lake Storage could be done with just three activities, shown below.

There are several good recommendations here; read the whole thing.

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Cathrine Wilhelmsen continues a series on Azure Data Factory. First, we get to see how to copy data from on-prem SQL Servers:

In the previous post, we looked at the three different types of integration runtimes. In this post, we will first create a self-hosted integration runtime. Then, we will create a new linked service and dataset using the self-hosted integration runtime. Finally, we will look at some common techniques and design patterns for copying data from and into an on-premises SQL Server.

And when I say “on-premises”, I really mean “in a private network”. It can either be a SQL Server on-premises on a physical server, or “on-premises” in a virtual machine.

Then, we learn how to run SSIS packages in Azure Data Factory:

Two posts ago, we looked at the three types of integration runtimes and created an Azure integration runtime. In the previous post, we created a self-hosted integration runtime for copying SQL Server data. In this post, we will complete the integration runtime part of the series. We will look at what SSIS Lift and Shift is, how to create an Azure-SSIS integration runtime, and how you can start executing SSIS packages in Azure Data Factory.

I’m going to guess that the next post will be all about the third integration runtime.

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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.

Second, using annotations to customize views:

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.

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Cathrine Wilhelmsen continues a series on Azure Data Factory by looking at triggers:

One important thing to note is that all times are in UTC. And since UTC does not observe daylight saving time… Well, let’s just say that if you need to execute pipelines during the workday and you have business users waiting for data, you may want to plan some trigger maintenance on the days when you fall back or spring forward. I know. Ugh 🙂 I’m hoping for better timezone support in the future 🤞🏻

Schedule triggers and pipelines have a many-to-many relationship. That means that one schedule trigger can execute many pipelines, and one pipeline can be executed by many schedule triggers.

Time-based triggers aren’t the only options, however—Cathrine also looks at the other three possibilities.

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Cathrine Wilhelmsen shows us how to debug Azure Data Factory pipelines:

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.

Turns out there’s more to it than clicking a button.

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Cathrine Wilhelmsen continues a series on Azure Data Factory:

The other way to build this solution is by creating an orchestration pipeline with two execute pipeline activities. This gives us a little more flexibility than having a single pipeline, because we can execute each pipeline separately if we want to.

Let’s start by creating a new pipeline and adding two execute pipeline activities to it. In the activity settings, select the pipelines to execute, and check wait on completion:

Read on for the demonstration.

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