![]() ![]() When you create your notebook, you can save it as a file in the location of your choosing, but it will not show up in the notebook section. What confused me about this initially is that you cannot create a simple notebook from the notebooks section in Azure Data Studio. This is not required as you can create a new notebook from the file menu or with the shortcut as noted on the screen in Azure Data Studio. Before we dive into this process too deeply, I want to be clear that we are going to create a Jupyter book to add our notebooks to. Let’s begin creating our first notebook in Azure Data Studio. Creating your first notebook in Azure Data Studio That section has a good mix of code and text to illustrate the power and capabilities of notebooks. Markdown file: Elastic job demo instructionsįor the purposes of this blog post, we will walk through the process of creating the original Jupyter book and the elastic query demo section.Markdown file: Elastic query demo instructions.Jupyter book: Azure SQL database elasticity.Here is the high level organization of the Jupyter book we are going to create: The notebook files are actual Jupyter notebook files which are split into sections for code and text. The markdown file is effectively a document that allows you to create a nicely formatted informational component for your notebook. Markdown files and notebooks are files created that are organized for particular purposes. ![]() Before we leave the structure and organization section here, I want to clarify that the book is the parent folder, and the section is a sub folder within the book. ![]() That folder will also contain several helper files to organize your notebooks, markdown files, and sections. When you create a Jupyter book, it looks like you’re creating a folder. ![]() Each time I tried to create my first Jupyter book, I didn’t understand what its purpose was in the beginning. While I am sure there are simple ways to create what we would like to do, I’m coming at this entirely from Azure Data Studio as a data developer not a data scientist. Once I started working with the notebook process in Azure Data Studio, I realized there were multiple components involved: While I may not get all of the terminology correct in this process, this is my discovery as I move forward through the process. Then I could go back and create the Jupyter book correctly from the beginning. So, as a newbie with notebooks and organization with Azure Data Studio, I created a notebook and a Jupyter book so I could see how the files are organized. I thought it should know where they should go. Part of my struggle in understanding what was happening is each time I tried to create a notebook it asked me for locations and files. The organization of notebooks and files in Azure Data Studio So, let’s go through the process of creating your first notebook step by step with explanations about what’s happening. For example, it was not entirely clear to me that one part of the process is creating a folder to store your notebooks with your markdown files and other content. This means that the instructions for how to create, organize, and use notebooks within Azure Data Studio is a bit lacking. One of the interesting things about working with notebooks, is that if you want to work with notebooks, it’s likely that you already have and you prefer to use them. How in the world do you work with notebooks in Azure Data Studio? Now that you have the background of what I was trying to accomplish, let’s look at the process I went through getting this done. I realized I could put together my entire demo package to share with the attendees and build the demo so that I could execute it a step at a time without highlighting. I watched a couple of demos on using notebooks and found some of the notebooks that have been created by Microsoft. I was also looking for better ways to automate the process, but more about that later. As I was working through testing my demo, I found executing code by highlighting and pushing “run” in either Data Studio or in SQL Server Management Studio was difficult because I struggled to control highlighting the code. Most of the code is ready to go since I have done this presentation a few times. I will be presenting a session on elastic queries in Azure SQL database. Let’s start with the core problem that I’m trying to solve today. ![]()
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