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Day: October 29, 2020

Understanding Digital Twins

Kurt Cagle explains a concept:

At the core of such digital twins is the notion of a model. A model, in the most basic definition of the word, is a proxy for a thing or process. A runway model, for instance, is a person who is intended to be a proxy for the viewer, showing off how a given garment looks. An artist’s model is a stand-in or proxy for the image, scene, or illustration that an artist is producing. An architectural model is a simulation of how a given building will look like when constructed, and with 3D rendering technology, such models can appear quite life-like. Additionally, though, the models can also simulate more than appearance – they can simulate structural integrity, strain analysis, and even chemistry interactions. We create models of stars, black holes, and neutron stars based upon our understanding of physics, and models of disease spread in the case of epidemics.

This is a really good explanation of the concept. Contrast with the explanation of, say, Azure Digital Twins. The first time I saw it, I thought one thing; then, when I read the intro page, I thought something different. Then, I walked through the demo and thought something yet again different. I might have just missed the part where it lays out exactly what a digital twin is and its importance but I do like Kurt’s explanation a lot more.

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Combining Two Survey Questions into a Graph

Stephanie Evergreen solves a challenge:

You’ve asked employees to rate a bunch of different aspects of their job. You want to know if they think that aspect is important AND how satisfied they are with that aspect of their job. So, naturally, you make two individual questions with response options like Not at all Important to Very Important and Not at all Satisfied to Very Satisfied. I would probably do the same thing.

But then you’ve got to show the data and, importantly, how those two variables – Importance and Satisfaction – relate to each other.

Click through for two methods of visualizing the results.

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Using Key Vault in Azure Databricks

Arun Sirpal shows us how easy it is to tie Azure Key Vault into Azure Databricks:

The key vault should always be a core component of your Azure design because we can store keys, secrets, certicates thus abstract / hide the true connection string within files. When working with databricks to mount storage to ingest your data and query it ideally you should be leveraging this to create secrets and secret scopes.

Click through for a demo.

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Detecting Spinlock Contention in SQL Server

Michael J. Swart walks us through spinlock contention:

When that occurred, the number of batch requests that the server could handle dropped significantly. So we saw more CPU use, but less work was getting done.

The high demand kept the CPU at 100% with no relief until the demand decreased. When that happened, the database seemed to recover. Throughput was restored and the database’s metrics became healthy again. During this trouble we looked at everything including the number of spins reported in the sys.dm_os_spinlock_stats dmv.

The spins and backoffs reported seemed extremely high, especially for the category “XVB_LIST”, but we didn’t really have a baseline to tell whether those numbers were problematic. Even after capturing the numbers and visualizing them we saw larger than linear increases as demand increased, but were those increases excessive?

Read on for the answer.

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PIVOT in Powershell

Shane O’Neill succumbs to peer pressure:

I can’t very well give out to people for not doing the right thing first time, even if it’s more difficult, if I don’t do the right thing myself!

As Kevin mentioned, once the data was in a proper format, a format designed for SQL, the calculations were trivial.

However, outputting the results in the same way in PowerShell required a way to pivot results in PowerShell. Thanks to some heavy lifting from Joel Sallow ( Blog | Twitter ), I now know how to pivot in PowerShell!

Here’s hoping that this post will help explain it for you also.

It’s interesting to see how much more difficult it is to turn a “tall” data set into a “long” data set in Powershell. It’s not that many lines of code once you know how to do it, but getting there is a challenge.

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