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Category: Azure Data Studio

Comparing SSMS and Azure Data Studio

Published 2021-02-10 by Kevin Feasel

Deborah Melkin contrasts SQL Server Management Studio with Azure Data Studio:

Honestly, the vast majority of my time is split between Management Studio (SSMS) or Azure Data Studio. I’m pretty simple\straightforward this way. I started playing a lot more with Azure Data Studio over the past year, but I find I’m not able to make the switch to using it full time. It really depends on the task that I need to do.

So what tasks do I do often and which tool do I use?

The plus side for Azure Data Studio is that it’s far enough along that some of these choices are difficult to make. The minus side is that it’s still often on the losing end. I’d expect that shift to continue over the next couple of years as the product matures and becomes a good product for database developers.

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Deploying ADS Database Projects Manually

Published 2021-01-06 by Kevin Feasel

Elizabeth Noble continues a series of videos on database projects in Azure Data Studio:

This week, we’ll talk about one of the easier ways to deploy your database changes. One of the benefits of database projects is that they can generate data-tier applications (DAC). The data-tier applications can be bundled into what is called a DACPAC. This is a collection of files that can be used to deploy your database.

Click through for the video.

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Linking between Notebooks in Azure Data Studio

Published 2020-12-23 by Kevin Feasel

Julie Koesmarno shows us the rules of linking notebooks in Azure Data Studio:

When writing a notebook, it can be very handy to be able to refer to a specific part to a notebook and allow the readers to jump to that part, i.e linking or anchoring. Using this technique, you can also create an index list or a table of contents or cross-referencing to parts of other notebooks too. Check out my demo notebook for this linking topic, from MsSQLGirl Github Repo.

Read on for those rules.

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December 2020 SQL Tools Releases

Published 2020-12-18 by Kevin Feasel

Drew Skwiers-Koballa gives us an update on where SQL Server tooling is at:

The December releases of Azure Data Studio 1.25 and SQL Server Management Studio (SSMS) 18.8 are now generally available.  Additionally, the mssql extension for Visual Studio Code has recently been updated to version 1.10.0. Read on to learn more about each of these updates and grab the latest versions of SSMS, Azure Data Studio, or the mssql extension for VS Code.

Read on to learn more.

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Cross-Cluster and Cross-Service Kusto Queries in ADS

Published 2020-12-17 by Kevin Feasel

Julie Koesmarno shows off some new functionality in Azure Data Studio:

This blog post covers examples of cross-cluster and cross-service querying, including handy syntax, code snippets and notebooks that you can use in Azure Data Studio.

As some of you may already know, Kusto (KQL) extension is available in Azure Data Studio, which allows you to explore Azure Data Explorer (ADX) more natively. ADX also supports cross-cluster and cross-service queries between ADX, Azure AppInsights and Azure Log Analytics. This cross- service query preview feature is documented in Query data in Azure Monitor using Azure Data Explorer.

Click through for the demos.

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Kusto Queries in Azure Data Studio Notebooks

Published 2020-11-30 by Kevin Feasel

Julie Koesmarno shows off the Kusto Query Language magic in Azure Data Studio notebooks:

To do this, you’ll need to ensure that you have Kqlmagic installed. See Install and set up Kqlmagic in a notebook. Then in a notebook, you can load Kqlmagic with %reload_ext Kqlmagic in a code cell.

The next step is then in a new code cell, you can start connecting to a Log Analytics workspace. There are three ways to do so (roughly – as I’m also learning in this space too):

1. Using Azure Active Directory Device Login authentication.
2. Using Az CLI login
3. Using Client Secret

Read on for one example using Azure AD authentication.

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Creating Jupyter Books in Azure Data Studio

Published 2020-11-17 by Kevin Feasel

Drew Skwiers-Koballa takes us through creating and deploying Jupyter Books:

The notebook experience in Azure Data Studio allows users to create and share documents containing live code, execution results, and narrative text. Potential usage includes data cleaning and transformation, statistical modeling, troubleshooting guides, data visualization, and machine learning. Jupyter books compile a collection of notebooks into a richer experience with more structure and a table of contents.  In Azure Data Studio we are able not only to use Jupyter books but also create and share them. Learn the basics of notebooks in Azure Data Studio from the documentation and read on to learn how to leverage a GitHub Action to publish and share remote Jupyter books.

Click through for the process of creating, opening, and distributing Jupyter Books.

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