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Author: Kevin Feasel

Understanding Data Mesh

Published 2022-03-15 by Kevin Feasel

Rick Spurgeon has a video series:

Decentralized architectures continue to flourish as engineering teams look to unlock the potential of their people and systems. From Git, to microservices, to cryptocurrencies, these designs look to decentralization as a method of breaking apart centralized bottlenecks. Data mesh is an approach to data and organizational management centered around decentralizing control of data itself. In this post, we’ll look at a Confluent Developer video-led course that tackles the big concepts and walks you through creating your own data mesh using event streams and Confluent Cloud.

Click through for a 9-part video series.

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Measurement Units in R

Published 2022-03-15 by Kevin Feasel

K.H. Kim has an article on unit conversion in R:

Data mtcars has a column named mpg. mpg means miles per gallon. ‘Mile’ and ‘gallon’ are units for length and volume. A mile is approximately 1.6 kilometers and a gallon is approximately 3.7 liters. Mile and gallon sound unfamiliar to people who live outside England or U.S.A. because international standard units for length and volume are meter and liter.

In this post, we will learn how to convert a unit to another unit, for instance, we will convert mpg to km/L, which is more comprehensible to people who use SI units

This is something that I’m surprised languages don’t do more of. F# has the concept of units of measure but these are compile-time—at runtime, you can still break the rules. And in most languages, type systems are so simplistic that it’s all just plain decimal math. H/T R-bloggers.

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Returning Blank instead of Zero in Power BI

Published 2022-03-15 by Kevin Feasel

Marco Russo does a bit of transformation:

In matrix visuals, Power BI usually hides rows where all the measures return a blank value. To leverage this behavior or simply to change the visualization of a measure depending on its result, you might want to achieve one of the following:

– Transforming a blank result to zero: this is covered in the article, How to return 0 instead of BLANK in DAX.

– Transforming a zero result to blank: this is the scenario described in this article.

Click through to see what the process looks like.

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Working with SIDs in Powershell

Published 2022-03-15 by Kevin Feasel

Jeffery Hicks translates security identifiers:

As usually happens during my day, I get sidetracked to another issue, and before you know it, I have a new PowerShell tool. In this instance, I was looking at event logs using Get-WinEvent. One of the event record properties is a UserID.

That’s very nice, but who is this? In this particular instance, the UserID property is SecurityIdentifier object.

Read on to see how to translate a SecurityIdentifier into something human-understandable.

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First Operator Execution Plan Properties

Published 2022-03-15 by Kevin Feasel

Grant Fritchey implores us not to forget the SELECT/INSERT/UPDATE/whatever operator:

I’ve watched several people recently go straight to XML when reading execution plans because they didn’t know about the execution plan properties in the first operator. Now, don’t get me wrong. If going straight to the XML is working for you, that’s fine. Keep doing it. I’m absolutely not questioning how anyone does things. I just want people to know that “hidden” information isn’t so much hidden as much as it’s not too obvious.

Read on to see what this contains.

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WEEKNUM() in Power Query

Published 2022-03-15 by Kevin Feasel

Imke Feldmann translates another Excel function:

If you are looking for the Excel equivalent of the WEEKNUM function for Power Query this article is for you.

As this is also a function that has many regional options, I was lucky to find an algorithm that I could use for its main part here: M functions to convert between ISO 8601 Week & Year ⇄ dates (e.g., 2014-12-29 ⇄ "2015-W01-1") (github.com)

Read on for the code and details on function parameters.

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Azure Functions and Azure SQL Database

Published 2022-03-14 by Kevin Feasel

Rajendra Gupta builds a simple Azure Function:

As a Platform as a Service (PaaS) service, Azure SQL Database enables developers to deploy SQL Database in Azure Cloud without managing the infrastructure. We use SQL Server Agent to schedule jobs to run at a specific schedule in an on-prem SQL instance. However, Azure DB does not have agent functionality.

There are multiple ways to schedule job or batch processes in the Cloud. You can explore the Azure automation series for executing scripts using Azure Logic apps and automation runbooks.

This article focuses on the Azure functions for scheduling a job for Azure SQL Database.

Read on for the process.

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Azure SQL News

Published 2022-03-14 by Kevin Feasel

Anna Hoffman brings us tidings:

Emily Lisa kicked it off by telling us about the latest Azure SQL Database product updates including Azure SQL maintenance windows, zone redundancy for SQL Hyperscale, and configurable backup storage redundancy for SQL Hyperscale.

Dani Ljepava from the Azure SQL Managed Instance product team came on to share a range of new features announced at SQL Bits now available in public preview: Link feature for Managed Instance, data virtualization with ADLS, Advanced notifications for planned events, Hybrid Service Broker, Windows authentication for Azure AD Principals, and the general availability of Maintenance windows. Watch the episode to see a special look at Link feature for Managed Instance!

Pam Lahoud also came on to talk about about the latest improvements that help you manage your SQL Server in Azure Virtual Machines, including the newly released SQL best practices assessment, all available to you by installing the free SQL IaaS Agent extension!

Read on for plenty more links and updates.

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When Compute Scalars Attack

Published 2022-03-14 by Kevin Feasel

Erik Darling goes scalar hunting:

This is awful. It really is. It’s so awful. These operators skated by undetected for years. Acting so innocent with their 0% cost.

Subprime operators, or something.

In this post, I’m going to show you how compute scalars hide work, and how interpreting them in actual execution plans can even be tricky.

I’m now imagining Erik as the Steve Irwin of the query tuning world. And it’s kind of working for me.

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