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Category: Azure Data Studio

Azure Data Studio June 2020 Release

Alan Yu announces a new release of Azure Data Studio:

The Data Virtualization extension for Azure Data Studio is now updated with more functionality and a new logo. This update allows you to use the data virtualization wizard to virtualize MongoDB and Teradata data sources into your SQL Server. This new functionality is available for SQL Server 2019 instances running CU5 or later.

To install the extension, search for Data Virtualization in the extension viewlet in Azure Data Studio and click install.

Of course I’m going to clip the bit about PolyBase.

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Installing Azure Data Studio on CentOS

Sreekanth Bandarla walks us through installation and usage of Azure Data Studio on CentOS:

Okay…now what? Where to locate the executable and how do I open Azure data studio in CentOS? GUI in CentOS is not as user friendly as you can see in some other Linux OSs (Mint for eg or few other Ubuntu flavors of Linux). In windows you can locate the program in start menu or even in few desktop experience Linux distributions it’s extremely easy to just search in application center, but that was not the case for me in CentOS 7.

Click through to see how to install and open ADS on a Red Hat-based system.

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SQL Server on a Windows Container

Kevin Chant lives dangerously:

In this post I want to cover an interesting Windows Container with SQL Server installed experiment that I did. Because it was fairly involved, and it took a while.

In fact, this is the experiment I was talking about in my recent post about recent Azure Data Studio updates. Which you can read about in detail here.

My general philosophy is to avoid Windows containers at all costs, though I’m glad that there are some more adventurous than I.

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May 2020 Release of Azure Data Studio

Alan Yu has some goodies for us:

The key highlights to cover this month include:

– Announcing Redgate SQL Prompt extension
– Announcing the new machine learning extension
– Added new Python dependencies wizard
– Added support for parameterization for Always Encrypted
– Improvements to the notebook markdown toolbar
– Bug fixes

For a list of complete updates, refer to the Azure Data Studio release notes.

I’ll have to check out the ML extension.

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Creating Charts with Azure Data Studio

Rajendra Gupta walks us through chart creation with Azure Data Studio:

Usually, we use to extract the data from a SQL database, copy it in Microsoft excel and creates the required Chart from it. We can also use various tools such as SQL Server Reporting Service ( SSRS), Power BI to import data and create charts, visuals from it directly. These tools work fine; however, it requires additional steps to install these tools, have intermediate knowledge of it. You might require to do this with different data set, and every time, you cannot create a separate visual using Power BI or SSRS. In this type of requirement, the most common useful tool is Microsoft Excel. You can also use PowerShell, but it again requires you to have PowerShell script knowledge. You can go through the article How to create charts from SQL Server data using PowerShell to create charts from PowerShell.

In this article, we will explore creating charts from SQL Server data without exporting it to separate tools Microsoft Excel, SSRS or Power BI.

There is some nice functionality available for quick analysis, though I’m disappointed that I can’t choose which column(s) to include in the visual—it looks like it simply includes them all. SandDance does, though its style precludes certain types of visuals like line charts.

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Azure Data Studio April 2020 Release

Alan Yu announces the April 2020 release of Azure Data Studio:

KQL magic extension support is now available in Azure Data Studio Notebooks. It allows you to connect, query and explore Azure Data Explorer (Kusto), ApplicationInsights and LogAnalytics data using kql (Kusto Query Language). If you are using Log Analytics today for your Azure SQL DB as described here, you can now do log metric analysis using KQL magic in Azure Data Studio Notebooks. 

KQL magic package can be downloaded from Manage Packages in Python Notebook or using pip install. In a Python Notebook in Azure Data Studio, load KQL magic using (%reload_ext Kqlmagic). Start connecting, querying, and exploring using %kql or %%kql for multi-lines.   

KQL magic allows you to see tabular results similar to SQL Notebook, where you can also have the benefits of exporting outputs to other formats (csv, Excel, JSON, XML) and using the Charting functionality. You can also take advantage of rendering charts directly with plotly for richer interactivity. 

There are several fairly big changes in here, so check them all out.

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Working with Jupyter Books in Azure Data Studio

Jamie Wick takes us through using Jupyter Book in Azure Data Studio:

The first thing to know is that Jupyter Books and “Jupyter Book support” (in Azure Data Studio) are slightly different concepts. Jupyter Books let you build web-based collections of Jupyter notebooks. Jupyter Books support allows you to build collections of Jupyter notebooks on your local computer or network (ie. not web-based). Additionally, all of the standards and functionality of the online Jupyter Books may not be fully supported/implemented in Azure Data Studio.

Click through for what this means as well as what the March 2020 release brought us.

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Using Jupyter Notebooks in SQL Agent Jobs

Rob Sewell shows us how to run an Azure Data Studio notebook as a SQL Agent job:

Azure Data Studio is a great tool for connecting with your data platform whether it is in Azure or on your hardware. Jupyter Notebooks are fantastic, you can have words, pictures, code and code results all saved in one document.

I have created a repository in my Github https://beard.media/Notebooks where I have stored a number of Jupyter notebooks both for Azure Data Studio and the new .NET interactive notebooks.

Another thing that you can do with notebooks is run them as Agent Jobs and save the results of the run.

Read on to learn how.

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Azure Data Studio March 2020 Release

Alan Yu announces the March 2020 release of Azure Data Studio:

Now you can add visualizations using a T-SQL query. In addition, as the gif illustrates, you can also customize your visualization whether it is a scatter or time series graph.

You can also copy your visualization or save the image so that you can quickly add this in an email or report to other team members.

We will continue to bring improvements to charting over the next few months.

They’ve put a lot of time and effort into notebooks. They’re still missing some of the quality of life improvements I want to see before moving to them full-time, but they’re consistently getting better.

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Snapshot Creation in Azure Data Studio

Dave Bland checks out an extension to Azure Data Studio to manage snapshots:

Like many Azure Data Studio extensions, DB Snapshot Creator is designed to bring functionality into ADS that is not present by default.  This extension was developed by Sean Price. As the name suggests, this extension can be used to easily create database snapshots.  Before going too deep into this extension, let’s take a quick moment to go over what a snapshot is.

Back in the day, I created a WPF tool for a company to manage snapshots for manual testing: take a snapshot, perform whatever destructive testing you needed to do, and revert back to a known good state. In a world with good CI/CD tooling and Docker containers, that’s not nearly as important anymore, but sometimes you just need to run a quick test, so I’m glad the functionality is still around.

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