Jupyter Notebooks bring a wonderful capability to hand someone a single file that contains both code, and instructions on how to run that code. This is great and can be used in many different ways, one of which is to help new team members get up to speed. But what happens when you need to do the same thing as one of your existing Notebooks, but now you need to do it at scale? What if you could take your existing Notebook and add parameters for things like Server name & Database? In this episode with Aaron Nelson, take a look at how new features in Azure Data Studio can help you take your Notebooks to the next level of re-usability.
[00:58] Notebooks overview
[02:54] Create a Parameterized Notebook- Demo
[03:51] SQL-on-Linux instance in a Docker container - Demo
[09:00] Export Power BI Workspace Assets with PowerShell - Demo
[12:22] Getting started
Parameterization of Notebooks in Azure Data Studio - Azure Data Studio | Microsoft Docs
Use Invoke-ExecuteNotebook to build a SQL-on-Linux instance in a Docker container, by calling Invoke-ExecuteNotebook to execute the Notebook, and passing in the sa_password & digits for the name/port number to the Notebook as a parameter