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Curated SQL Posts

Performing an Azure DevOps Release for a Database Project

Olivier Van Steenlandt pushes out the database:

In my previous blog post, Automate your Database Builds – Using Azure DevOps Pipelines, we successfully automated our Build process. As a next step, we will be automating our deployment process to Azure SQL.

In this blog post, we will go through the process step-by-step to set up our Release pipeline in Azure DevOps and execute our first release just by clicking a button.

Olivier has plenty of screenshots to take us through the process.

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Azure Data Studio 1.42 Released

Erin Stellato shows off what’s new in Azure Data Studio:

I just finished writing up the release notes for Azure Data Studio 1.42, and I cannot get over the amount of work the engineering team has completed since January.  Go big or go home.

For regular users of Azure Data Studio, we hope you are starting to experience the improvements we have been making in the application.  This continues with the 1.42 release, though we also found time to introduce new functionality as well. 

Read on for some of the highlights.

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Refreshing a Power BI Dataset via HTTPS URL

Gilbert Quevauvilliers presses the big red button:

I have found that sometimes there are other systems that are loading data, and once they are complete they then want to refresh the Power BI Dataset.

Another way to do this is to use Power Automate, in which a system or user can request a HTTPS URL and once called that will then refresh the Power BI dataset.

I explain how to do this in the steps below.

Click through to see how to set up that job.

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Analyzing SQL Server Table Metadata

Barney Lawrence looks at details about a table:

For a while I’ve been building up a script that uses SQL Servers INFORMACTION_SCHEMA.COLUMNS table to create a query that applies aggregates to each column in a table and then formats the results to allow an at a glance summary of the shape of the data inside of it.

I showed some of the techniques used to build this in a session regarding metadata and data warehousing which I co-presented with Emma Dolling and Ruth Pearson at SQLBits. The one comment that I heard the most after this session was “I want that script”. I did promise to make it available more than a few times and so I’ve put together a github repository to hold it and other resources we built for the session.

Barney anticipated the most likely change I’d make while reviewing the script: APPROX_COUNT_DISTINCT() if you’re on SQL Server 2022 and dealing with a large table.

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Using the OpenLibrary ISBN API with Powershell

Robert Cain has been working on a neat project:

In this post we’ll begin with an overview of what an ISBN is. We’ll then talk about the website that will be the source for our data. It has two different web APIs (Application Programming Interface) that we can use. We’ll discuss one here, then in the next blog post cover the advanced version.

First though, if you haven’t read the introductory post in this series, The ArcaneBooks Project – An Introduction, I’d highly recommend doing so as it lays the foundation for why and how this project to get ISBN data originated.

Robert is building this up over a series of posts, so stay tuned.

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Data Validation with Great Expectations and Azure Functions

Eduard van Valkenburg does a bit of data validation:

Great Expectations (Great Expectations Home Page • Great Expectations) is a popular Python-based OSS tool to validate data that comes into your data estate. And for me, validating incoming data is best done file by file, as the files arrive! On Azure there is no better platform for that then Azure Functions. I love the serverless nature of Functions and with the triggers available for arriving blobs, as well as HTTP, event grid events, queues and others. There are some great patterns that allow you to build event-driven data architectures. We also now have the Python v2 framework for Azure Functions available, which makes the developer experience even better. So, let’s go through how to get it running.

This looks really interesting and tying it in to Azure Functions is a good idea assuming that the checks don’t run for too long.

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Pro Encryption in SQL Server Errata

Matthew McGiffen made the first mistake—admitting fault for anything, ever:

My biggest fear when my book went into production was that any factual errors had slipped through my checks and the various reviews. I had a lot of reviewer support from Apress, but at the end of the day any issues are my responsibility.

So far I’m not aware of any factual errors but one kind reader (Ekrem Önsoy) has shared with me a few typos they have found. I’m going to document them here and will keep this post up to date as I’m made aware of any others:

Mistakes in 300 pages of text will happen, no matter how many times you go through your magnum opus. For example, I hate the fact that I went through every chapter of PolyBase Revealed 8 or 9 times to weed out any little typo. Then, as soon as I got my copies of the print edition in, I flipped open to a random page and immediately spotted a typo.

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An Overview of SQL Server Security Options

Ben DeBow gives us a once-over of things you can do to harden a SQL Server instance:

Microsoft SQL Server is one of the most secure platforms available, but companies need to deploy, configure, and implement it correctly – along with implementing its built-in security features – in order to ensure their systems are fully protected. Here, we’ll explore six of the most important security features and how to implement them to enhance your SQL Server security.

This isn’t a how-to guide so much as it is a what-you-can-do guide.

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