Jupyter Notebooks

Introduction

Jupyter Notebooks are a sleeping giant, poised to change nearly everything involved with data, analysis, and related processing. Born from Data Science, Jupyter Notebooks combine rich text, images, links, and code (for example, SQL) along with persistent query results. These notebooks are fully packaged in a convenient and reusable text (JSON) file, which can also be stitched together, forming a book containing related readme, and navigational .yml files. This groundbreaking capability is deeply integrated into Azure Data Studio and is destined to have a broad impact with many of your interactions in SQL, Python, PowerShell, and other languages while working with data and processes.

Notebook building blocks

The two primary components found in notebooks are ‘code’ and ‘text’ cells. This is similar to traditional SQL coding that often results in ‘code’ interspersed with ‘comments’ whether in-line or as introductory text blocks. That said, it is pretty much where the similarities stop as notebooks build dramatically on this foundation.

This chapter is supported by Notebooks which you can be download from the course GitHub repository. If this is the first time you have cloned a repository in Git, please jump ahead to the Source Control by Design chapter for a step-by-step walkthrough of getting started with Git and GitHub.

To help you get started with Notebooks, we have added additional content in the appendix that demonstrates building a SQL Notebook in Azure Data Studio.