A Fine Slice Of SQL Server
Maelle Salmon does a bit of mocking:
This blog featured a post on mocking, the art of replacing a function with whatever fake we need for testing, years ago. Since then, we’ve entered a new decade, the second edition of Hadley Wickham’s and Jenny Bryan’s R packages book was published, and mocking returned to testthat, so it’s time for a new take/resources roundup!
Click through to see how you can create mocks in R as well as some practical examples of mocks in action.
Comments closedDan Oehm notes another brick in the wall:
If you’re looking for something a little different,
ggbrickcreates a ‘waffle’ style chart with the aesthetic of a brick wall. The usage is similar togeom_colwhere you supply counts as the height of the bar and afillfor a stacked bar. Each whole brick represents 1 unit. Two half bricks equal one whole brick.It has been available on Git for a while, but recently I’ve made some changes and it now has CRAN’s tick of approval.
Click through to see how you can use it. This style of waffle chart, in the right scenario, can be quite useful, providing a high-level view and also giving you some idea of fine-grained magnitudes. H/T R-Bloggers.
Comments closedWe are excited to announce that Copilot in Azure SQL Database is now in limited public preview! Copilot in Azure SQL Database experiences are designed to streamline the design, operation, optimization, and health of Azure SQL Database-driven applications. It improves productivity in the Azure portal by offering natural language to SQL conversion and self-help for database administration.
Click through to see what you can do with it right now. I’d expect there will be more capabilities over time, too.
Comments closedJohn Cook shares an example of a hash collision:
Marc Stevens gave an example of two alphanumeric strings that differ in only one byte that have the same MD5 hash value. It may seem like beating a dead horse to demonstrate weaknesses in MD5, but it’s instructive to study the flaws of broken methods. And despite the fact that MD5 has been broken for years, lawyers still use it.
Click through for the example.
Comments closedJulia Liuson talks about big licensing news:
Today, Redis, a leading provider of in-memory data store solutions, announced a transition in its open-source licensing approach. Redis is moving away from the BSD 3-Clause License to a dual-license model, offering developers the choice between the Redis Source Available License version 2 (RSALv2) or the Server-Side Public License version 1 (SSPLv1).
Read on to learn what this means for Azure Cache for Redis.
Comments closedJonathan Katz share some thoughts:
(Disclosure: I’m on the PostgreSQL Core Team, but what’s written in this post are my personal views and not official project statements…unless I link to something that’s an official project statement 😉
I was very sad to learn today that the Redis project will no longer be released under an open source license. Sad for two reasons: as a longtime Redis user and pretty early adopter, and as an open source contributor. I’ll preface that I’m empathetic to the challenges of building businesses around open source, having been on multiple sides of this equation. I’m also cognizant of the downstream effects of these changes that can completely flip how a user adopts and uses a piece of technology.
Read on to learn about why Jonathan believes (with good reason) Postgres’s license is a lot more sticky.
Comments closedSteven Sanderson is about to cut somebody:
In the realm of data analysis, understanding how to effectively segment your data is paramount. Whether you’re dealing with age groups, income brackets, or any other continuous variable, the ability to categorize your data can provide invaluable insights. In R, the
cut()function is a powerful tool for precisely this purpose. In this guide, we’ll explore how to harness the full potential ofcut()to slice and dice your data with ease.
Read on for examples of how to use the cut() function.
When you’re visualizing categorical data, sorting the bars in your chart is usually a straightforward task. Or is it?
In most cases, you probably take the category with the largest value and stick that in the prime spot, the leftmost slot on the horizontal axis. Then, you proceed from left to right in descending order of value. Easy peasy.
But it’s not always that simple, as Mike points out.
Comments closedRod Edwards has a process for that:
Nobody likes to do the same monontonous task over and over again. Well, saying that, maybe some out there do in order to look and feel busy…but I don’t, as I nearly always have something else more pressing or fun or interesting to do. By automating those repeatable tasks, it reduces boredom, chance of errors, and stress if you’re already a busy bunny.
This is where Automation comes into play, and in Azure we have a few options. This post focuses in Azure automation.
Read on to see how Azure Automation works and how to build a Powershell runbook in it.
Comments closedEdwin Sanchez shows a variety of methods to calculate percentages of the whole in T-SQL:
Calculating percentages in SQL Server is like slicing a pie. You need to know the total size (the denominator) and the size of the slice you want (the numerator). To get a percentage, you divide the slice size by the total size and multiply by 100.
Read on for a variety of methods to calculate this. I wouldn’t use all of the methods myself, as I have certain predilections against subqueries in the SELECT clause, but they do get the job done.