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Author: Kevin Feasel

ConstantCare Population Report for Fall 2023

Published 2023-10-19 by Kevin Feasel

Brent Ozar shares some percentages:

For the long story: ever wonder how fast people are adopting new versions of SQL Server, or what’s “normal” out there for SQL Server adoption rates? Let’s find out in the summer 2023 version of our SQL ConstantCare® population report.

Out of the thousands of monitored SQL Servers, a whopping 44% are SQL Server 2019! That’s the highest percentage we’ve seen for any version in the 3 years that we’ve been doing this analysis.

Standard statistical sampling rules apply, though there is an interesting note in the comments about how EC2 instances break down in AWS by version of SQL Server, and the numbers are reasonably similar.

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Case Sensitivity in Power BI

Published 2023-10-19 by Kevin Feasel

Kurt Buhler is going to raise my blood pressure this morning:

Most Power BI models are case-insensitive, meaning that “Bonk” is the same as “BONK”. However, Power BI data models can also be created as case-sensitive if you create a Direct Lake model in Fabric, or create a new model with external tools and enter a case-sensitive collation property. Two otherwise identical models which differ only in this case-sensitivity may produce different results, even though they’re using the same data, DAX, relationships, and tables.

It’s useful to know how case-sensitivity affects your model and its query results. You should also be able to identify and validate whether your model is case-sensitive. This is particularly important in the following scenarios:

Read on for those scenarios and how you can fix the problem of case sensitivity. My official stance on case sensitivity, by the way, is that applications should be case-insensitive on input but retain casing on output, so “dog” = “Dog” = “DOG” for sorting and querying, but if I saved “Dog” then that’s what should display.

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Whitepapers for Oracle and SQL Server in Azure

Published 2023-10-19 by Kevin Feasel

Kellyn Gorman has been busy:

I’ve been pretty busy with work and travel, but I finally got an official Silk Github repository to publish a couple new white papers and sizing assessment worksheets for customer access.  These are primarily Oracle and SQL Server to Azure focused white papers, but I will be publishing ones on GCP next, to be followed by AI and other database platforms soon.

Click through for links to the documents.

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(Near)-Real-Time Analysis with Microsoft Fabric

Published 2023-10-19 by Kevin Feasel

Reza Rad continues a series on Microsoft Fabric:

Microsoft Fabric offers a workload for real-time solutions. Real-time Analytics can be used for streaming data, such as the data coming from IoT devices. It can be used not only to ingest the data but also to analyze it and use it for other Fabric workloads, such as data science. In this article and video, you will learn what is Real-Time Analytics in Microsoft Fabric and how it works.

Read on for a detailed demo.

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Time Series Stationarity Testing in R

Published 2023-10-18 by Kevin Feasel

Steven Sanderson isn’t just spinning in place:

Before we delve into the ts_adf_test() function, let’s understand the concept behind it. The Augmented Dickey-Fuller (ADF) test is a crucial tool in time series analysis. It’s like the Sherlock Holmes of time series data, helping us detect whether a series is stationary or not. Stationarity is a fundamental assumption in time series modeling because many models work best when applied to stationary data.

So, why “Augmented”? Well, it’s an extension of the original Dickey-Fuller test that accounts for more complex relationships within the time series data.

Click through to see how you can use the ts_adf_test() function to get a better feel for whether a time series is stationary.

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A Primer on A/B Testing for Engineers

Published 2023-10-18 by Kevin Feasel

John Mount performs some testing:

I’d like to discuss a simple variation of A/B testing in an engineering style.
By “an engineering style” I mean:

  • We will work a simulated example to see that the system works as claimed.
  • We will exhibit examples of problems before trying to fix them.
  • We will demonstrate all of the top level claims as calculations, and not delegate these to references.
  • We will leave fundamental math to the references, and not try to re-derive it.

In my opinion far too few A/B testing treatments check soundness, even on simulated data. This makes it easy for such articles to leave out important steps. If a relied on reference omits a step, the derived work may have to do the same.
We will implement the experiment design directly, instead of using a canned power calculator so we have a place to discuss some of the design issues in A/B test design.

This is an excellent dive into the topic and I highly recommend taking the time to read it.

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Running Apache Flink Jobs from HDInsight

Published 2023-10-18 by Kevin Feasel

Sairam Yeturi builds a streaming job:

Could you already complete creating your first Apache Flink® cluster and submit your streaming job on it with HDInsight on AKS?

Well, if you are yet to do that – Let me help you get started.

Click through for a step-by-step walkthrough on how to create a Flink-centric HDInsight cluster on Azure Kubernetes Service and how to create a new job, assuming you have the Jarfile for that job already.

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A Primer on Boyce-Codd Normal Form

Published 2023-10-18 by Kevin Feasel

I have a new video:

In this video, we drill into one of the two most important normal forms, learning what Boyce-Codd Normal Form (BCNF) is, how you can get to BCNF, and a practical example of it. We also learn why I cast so much shade on 2nd and 3rd Normal Forms.

Boyce-Codd Normal Form is one of the two most important normal forms, and I’m pretty happy with the way this video came together to explain how you can get from 1NF into BCNF, as well as the specific benefits this provides.

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Cloning Tables in Microsoft Fabric

Published 2023-10-18 by Kevin Feasel

Koen Verbeeck is interested:

A while ago I had a little blog post series about cool stuff in Snowflake. I’m starting up a similar series, but this time for Microsoft Fabric. I’m not going to cover the basic of Fabric, hundreds of bloggers have already done that. I’m going to cover little bits & pieces that I find interesting, that are similar to Snowflake features or something that is an improvement over the “regular” SQL Server.

To kick off this series, I’m going to start with a feature that also exists in Snowflake: zero-copy cloning. The idea is that you create a copy of a table, but instead of actually copying the data, pointers are created behind the scenes that just point to the original data. This means creating a clone is a metadata-only operation and is thus very fast. If you make updates against your clone, they will be stored separately, so in all purposes it seems you created a brand new table. Except you didn’t.

Read on to see how this works and what its current limitations are compared to Snowflake.

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