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Category: SQL Server and Azure

These posts are about the Microsoft Data Platform, including SQL Server and Azure products and services. Topics include T-SQL, SQL Server Integration Services (SSIS), SQL Server Data Tools (SSDT), SQL Server Management Studio (SSMS), Azure Data Studio (ADS), Azure Data Factory (ADF), Azure Databricks, Azure SQL Databases, Azure SQL Data Warehouse and Azure Data Lake Storage.

Preparing for and Taking Microsoft Exam DP-200 (Implementing an Azure Data Solution)

On January 31st, 2019, Microsoft released two new Azure Data exams: Implementing an Azure Data Solution (DP-200) and Designing an Azure Data Solution (DP-201). If you pass both of these exams, you become a Microsoft Certified Azure Data Engineer Associate. On May 24th, 2019, I passed DP-200: Implementing an Azure Data Solution! I am now halfway to my first Azure certification, yay :)

In this post, I share how I prepared for the exam and what my experience was like on the day of the exam.

Important Updates in June 2019

I passed exam DP-200 in May 2019, and published this post based on that version of the exam. In June 2019, the exam will be updated:

List of updates to Exam DP-200 in June 2019

While most of the content in this post should still be relevant, there will be some differences between my skills measured checklist and the skills measured in this updated version of the exam.

Preparing for Microsoft Exam DP-200: Implementing an Azure Data Solution

When I started preparing for the DP-200 exam in February 2019, it had just been released. There were no courses, practice exams, or books available at that time. Instead, I relied heavily on Microsoft Learn and a lot of hands-on experience.

In short, this is how I prepared:

On April 3rd, 2019, the official classroom course was released. There is also a self-paced course available on Microsoft Open edX, and all the course labs are available on GitHub. I did not take either of these courses or work through the labs because I wanted to try the new learning paths from Microsoft, but you may find the more traditional courses useful.

So! In addition to the things I did to prepare, you may also want to:

Below, you can find more details about how I prepared with links to all the resources I used.

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Comparing Mapping and Wrangling Data Flows in Azure Data Factory

In 2019, the Azure Data Factory team announced two exciting features. The first was Mapping Data Flows (currently in Public Preview), and the second was Wrangling Data Flows (currently in Limited Private Preview). Since then, I have heard many questions. One of the more common questions is “which should I use?” In this blog post, we will be comparing Mapping and Wrangling Data Flows to hopefully make it a little easier for you to answer that question.

Illustration of Person Comparing Mapping and Wrangling Data Flows in Azure Data Factory

Should you use Mapping or Wrangling Data Flows?

Now, we all know that the consultant answer to “which should I use?” is It Depends ™ :) But what does it depend on?

To me, it boils down to a few key questions you need to ask:

  • What is the task or problem you are trying to solve?
  • Where and how will you use the output?
  • Which tool are you most comfortable using?

Before we dig further into these questions, let’s start with comparing Mapping and Wrangling Data Flows.

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Video: Azure Data Factory Data Flows Introduction

Video: Azure Data Factory Data Flows Introduction

In January 2019, I was honored to be asked to contribute to the PASS Insights BI Edition Newsletter. I said yes, of course! :) I chose to create an Azure Data Factory Data Flows introduction video. This is a sneak preview of the upcoming Data Flows feature, with a quick walkthrough of how easy it can be to create scalable data transformations in the cloud – without writing any code!

Please note: As of January 2019, when I recorded this video and published this blog post, Azure Data Factory Data Flows is still in preview. Features will be added and things will get changed, just like all the other Azure products. But! Hopefully this shows what you can look forward to.

At the end of this blog post, I have tried to answer some frequently asked questions about Azure Data Factory Data Flows.

Video: Data Flows Introduction

Azure Data Factory Data Flows | Cathrine Wilhelmsen | BI Insights
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Creating a SQL Server 2019 Demo Environment in a Docker Container

Creating a SQL Server 2019 Demo Environment in a Docker Container

About a month ago, I learned something new. I learned how to run SQL Server 2019 in Docker and how to set up my demo environment in a container. Cool stuff! I like whales. Whales are cool.

While learning, I started writing this blog post. Then I got distracted and never finished it. This weekend, I had to set up my demo environment again. It was the perfect opportunity to update the content and finally publish this post.

(Why did I have to set up everything again? Oh, it’s a long story that involves disk cleanup and a Cathrine who likes to delete things to keep her computer tidy. Ok, it’s not really a long story. It was more like “oops, I accidentally deleted my container”.)

Anyway! Back to the actual content.

In this post, I share my approach and code snippets for:

  1. Installing Docker
  2. Getting SQL Server 2019
  3. Running SQL Server 2019 in a Docker Container
  4. Restoring Demo Databases (AdventureWorks and WideWorldImporters)

Installing Docker

I knew nothing about Docker or containers a month ago. But! I’m lucky to have smart friends :) Andrew Pruski (@dbafromthecold) wrote Running SQL Server 2019 CTP in a Docker container as part of his brilliant blog post series on containers.

I decided to start with his walkthrough and do exactly what he did. It worked pretty well for me! See below :)

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