Skip to content

Category: Data Platform

I’m a data geek 🤓 In fact, I like data so much that I have made it my career! I work with Azure Data and the Microsoft Data Platform, focusing on Data Integration using Azure Data Factory (ADF), Azure Synapse Analytics, and SQL Server Integration Services (SSIS).

In this category, I write technical posts and guides, and share my experiences with certification exams. You can also find a few interviews with Azure and SQL Server experts!

Azure Data posts may cover topics like Azure Data Factory, Azure Synapse Analytics, Azure SQL Databases, and Azure Data Lake Storage. Microsoft Data Platform posts may cover topics like SQL Server, T-SQL, and SQL Server Management Studio (SSMS), and SQL Server Integration Services (SSIS).

Parameters in Azure Data Factory

Woman standing next to a projector showing the Azure Data Factory logo.

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.

Templates in Azure Data Factory

Woman standing next to a projector showing the Azure Data Factory logo.

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!

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

Screenshot of the Azure Data Factory Home page, highlighting the create pipeline from template option

Source Control in Azure Data Factory

Woman standing next to a projector showing the Azure Data Factory logo.

Raise your hand if you have wondered why you can only publish and not save anything in Azure Data Factory 🙋🏼‍♀️ Wouldn’t it be nice if you could save work in progress? Well, you can. You just need to set up source control first! In this post, we will look at why you should use source control, how to set it up, and how to use it inside Azure Data Factory.

And yeah, I usually recommend that you set up source control early in your project, and not on day 19… However, it does require some external configuration, and in this series I wanted to get through the Azure Data Factory basics first. But by now, you should know enough to decide whether or not to commit to Azure Data Factory as your data integration tool of choice.

Get it? Commit to Azure Data Factory? Source Control? Commit? 🤓

Ok, that was terrible, I know. But hey, I’ve been writing these posts for 18 days straight now, let me have a few minutes of fun with Wil Wheaton 😂

Aaaaanyway!

Executing SSIS Packages in Azure Data Factory

Woman standing next to a projector showing the Azure Data Factory logo.

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.

(And if you don’t work with SSIS, today is an excellent day to take a break from this series. Go do something fun! Like eat some ice cream. I’m totally going to eat ice cream after publishing this post 🍦)

Copy SQL Server Data in Azure Data Factory

Woman standing next to a projector showing the Azure Data Factory logo.

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.

Or, in my case, “on-premises” means a SQL Server 2019 instance running on Linux in a Docker container on my laptop 🤓