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Author: Kevin Feasel

A Primer on Azure Arc-Enabled Data Services

Published 2022-08-18 by Kevin Feasel

Warwick Rudd has a four-parter on Azure Arc-Enabled Data Services. Part 1 sets the stage:

Utilising Azure Arc-enabled data services provides you the ability to take advantage of the Azure data services (SQL Server, Azure SQL Managed Instance, PostgreSQL) in a hybrid environment. This offering provides you with reduced administrative efforts in managing and maintaining your data services while giving you the same look and feel as if you were running in the Azure Cloud.

Part 2 looks at the Data Controller:

The Azure Arc Data Controller is a Kubernetes operator that performs all of the orchestration to ensure you achieve your desired state. This is the main component in the Azure Arc infrastructure that links the data services with the Arc-enabled hardware located either in your On-premises, Azure, or any other public cloud data center and your azure subscription.

The Arc data controller allows you to deploy, manage, secure, and monitor your deployed data services estate using Azure Data Studio or the Azure Portal (only for directly connected mode deployments) but giving you the same experience as if you were managing your data services from inside of the Azure Portal.

Part 3 deploys a Data Controller:

As previously mentioned there are 2 types of deployment available for your Arc Data Controller. In this post, we are going to have a look at deploying in the Arc Data Controller using the directly connected mode.

For a directly connected Arc Data Controller, we have direct connectivity to our Azure subscription. With this in mind, there are several options as we previously discussed on how to deploy the data controller. For this post, we are using the portal deployment method.

Finally, Part 4 covers management options:

With ADS open and running you can create connections to Arc Data Controllers the same as you can with Instances of SQL Server. In ADS we have under the connections area a section specific for Arc Data Controllers.

Check out all four posts.

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Power BI Field Parameters and None Options

Published 2022-08-18 by Kevin Feasel

Barney Lawrence votes None of the Above:


Field Parameters are one of my favourite recent additions to Power BI. The ability to turn a single chart into potentially dozens changes the way we think about putting variations of visuals on the page. It was a real wow moment for a client recently when I showed how field parameters for 5 fields and 5 measures could produce a single report page that replaced 25 of their existing reports.

While they theoretically don’t allow you to do much that you couldn’t previously with a disconnected slicer and a lot of DAX they build it faster and without the need to get heavily in to coding DAX. Anything that lowers the difficulty bar for users trying to make the most out of Power BI is a good thing in my book.

There are a couple of issues Barney has with them as they stand now but there are workarounds.

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Calculating the Hurst Exponent in R

Published 2022-08-17 by Kevin Feasel

Sang-Heon Lee does some analysis:

Pairs trading literature use the Hurst exponent frequently since it gives an simple and intuitive indicator for the behavior of stock returns. Using S&P 500 returns, let’s learn how to estimate it using R code manually and then use R package conveniently.

Click through for those two examples, as well as a more detailed explanation of the math driving this. H/T R-Bloggers.

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Interpreting Kernel SHAP

Published 2022-08-17 by Kevin Feasel

Michael Mayer digs into Kernel SHAP:

In their 2017 paper on SHAP, Scott Lundberg and Su-In Lee presented Kernel SHAP, an algorithm to calculate SHAP values for any model with numeric predictions. Compared to Monte-Carlo sampling (e.g. implemented in R package “fastshap”), Kernel SHAP is much more efficient.

I had one problem with Kernel SHAP: I never really understood how it works!

Needless to say, Michael knows Kernel SHAP a lot better now, considering there’s now a kernelshap package for us.

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T-SQL Tuesday 153 Roundup

Published 2022-08-17 by Kevin Feasel

Kevin Kline musters the troops:

I received a great collection of blog posts in response to T-SQL Tuesday 153 which I kicked off on Tuesday, August 2nd – asking you to write about a conference or event that had a significant event on their life. As one of the small handful of people who attended every PASS Summit from its founding through the pandemic lockdown, I’ve witnessed so many transformational experiences firsthand.

Human beings are social creatures and though we as IT pros like to focus on hard technology skills first and foremost, I think we can all admit that having a great social experience at a conference like the PASS Summit in North America, SQLBits in the UK, or Data Platform Summit in India is at least as important as the technical learning.

Read on for a summary of several posts.

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Migrating Databases between SQL Managed Instances

Published 2022-08-17 by Kevin Feasel

Etienne Lopes performs a migration:

In this post I’m going to show a very simple way to migrate a database between two SQL Server managed Instances in Azure. I’m not a big fan of bacpac files (although I work with it when necessary) so I’ll use a different approach here. Besides, when creating a bacpac file using SSMS there are some schema validations that occur at the beginning that will abort the bacpac generation for example if the database holds three-part names inside stored procedures. While not supported in SQL Azure DB it is supported in SQL Managed Instances (as are cross-database queries), and it can be quite frustrating to experience this show stopper when using bacpac’s to migrate or copy databases between Managed Instances.

Click through for the demo. And yeah, I’ve run into limiting factors with bacpacs, such as having certificates for encrypting data (even if you back those up separately).

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Adding an Existing Data Factory to GitHub

Published 2022-08-17 by Kevin Feasel

Andy Leonard has a three-parter for us. Part 1 shows you how to create a GitHub account and repo:

The unabridged topic of source control with github is beyond the scope of this post. There are a number of ways to accomplish the tasks described in this post and series. I welcome your suggestions in the comments.

This post is written to help Azure Data Factory developers get started using github.

Part 2 connects a Data Factory to the repository:

For the purposes of this demo, accept the defaults for “Publish branch” and “Root folder.” Check the “Import existing resources to repository” checkbox under the “Import existing resource” property, select the main branch in the “Import resource into this branch” property, and then click the “Apply” button:

Part 3 handles changes:

Applying what we’ve configured and learned thus far, let’s put this to work in a code-management workflow.

When it’s time to make a change, first create a new branch. I can hear some of you thinking, “Why, Andy? Why create a new branch?” That’s an excellent question. I am so glad you asked! Think of the new branch as a temporary copy of the current state of my Azure Data Factory. 

This series works from the assumption that you don’t have any real experience with Git (or GitHub) for source control, and maybe not much source control experience at all.

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Backup Jobs and Dropped Databases

Published 2022-08-17 by Kevin Feasel

Chad Callihan reasons through a use case:

I’m a big fan of Ola Hallengren’s SQL Server maintenance scripts and would recommend that anyone working with SQL Server check them out. They have served me well over the years. As it relates to today’s blog post, maybe too well…

I recently ran into a strange situation with the DatabaseBackup stored procedure that had me scratching my head: a backup job completing successfully for a database that didn’t exist.

Confused? So was I. Let’s take a look at how it happened.

Click through for the scenario.

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KQL StartOf Functions

Published 2022-08-16 by Kevin Feasel

Robert Cain continues a series on KQL:

In the previous post, Fun With KQL – DateTime Arithmetic, we had hard coded a date for the start of the year, in order to find out how much time had elapsed between it and datetime columns. I had mentioned there are ways to dynamically calculate such values.

In this post we’ll look at one way, using the StartOf... functions. These include startofyear, startofday, startofmonth, and startofweek.

Read on to see how they all work.

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