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Author: Kevin Feasel

Useful Operations in dbatools

Published 2024-01-04 by Kevin Feasel

Rod Edwards shows off some nice functionality in dbatools:

I often build solutions around the dbatools functions, the below is just some of my Operational favourites. With some I’ve included the output pipe that I use most frequently, but obviously, you can view and use the output however you choose to. Clearly, DBATools has many functions to add/remove/update SQL as well, but i’m just folking on the ‘gets’ here.

Naturally, as mentioned…its powershell, you can programmatically use this for any of your automation needs. Marvellous.

The ever growing list of commands can be found here: command index – dbatools . This can prove daunting to new users of the toolset, so here’s a starter for 10.

Click through for those 10.

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Math Operations in T-SQL

Published 2024-01-04 by Kevin Feasel

Daniel Hutmacher builds a few functions:

As part of spending waaaaaay to much time trying to solve the 2023 Advent of Code challenges, I came across multiple instances where I had to dust off some old math that I hadn’t paid attention to since I went to school back in the 90ies.

So for my own convenience, and yours, I’ve built functions for some common math that you might perhaps encounter at some point. I found this whole experience to be a great way to familiarize myself with a lot of the new functionality in SQL Server 2022, including GENERATE_SERIES(), LEAST(), GREATEST() and more. The Github repo contains a SQL Server 2019 version where I’ve built drop-in versions of the 2022 functions, but they probably won’t perform as well as the built-in stuff.

Click through for demonstrations of determining whether something is a prime number, finding the greatest common divisor and least common multiple, factorization, factorials, and even a bit of combinatorics.

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Thoughts on Clean Code

Published 2024-01-04 by Kevin Feasel

Chad Callihan resolves to write better code:

I’ve been involved with more official development work on top of database responsibilities in the last few months, which led to the recommendation to read Clean Code by Robert C. Martin. It’s an older book published in 2011, but plenty of rules and guidelines still apply. Along with the more technical details, one area jumped out at me in Chapter 12 that can apply to anyone writing code, queries, or scripts:

Click through for a pair of salient quotations and some more thoughts from Chad.

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Dynamic Search in SQL Server Stored Procedures

Published 2024-01-04 by Kevin Feasel

Erik Darling isn’t content with simple searches:

Like having a built-in type to make dynamic SQL more easily managed, it would also be nice to have some mechanism to manage dynamic searches.

Of course, what I mean by dynamic searches is when you have a variety of parameters that users can potentially search on, with none or few of them being required.

Erik provides two techniques and contrasts the two, so check it out.

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Foreign Key Discovery in SQL Server & Azure SQL DB

Published 2024-01-04 by Kevin Feasel

Josephine Bush walks around town with a lantern looking for a good foreign key:

There are plenty of times I’m called upon to fix data. To do this, I must know what dependencies are in the database. Foreign keys are a crucial aspect of maintaining data integrity within relational databases. They establish relationships between tables, ensuring data references remain consistent and accurate. In an Azure SQL Database, identifying and managing foreign keys is essential for maintaining a well-structured and reliable database architecture.

Click through for a primer on foreign key constraints, a few ways to find them, and some closing thoughts on working with tables containing foreign key constraints.

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Quantile Regression using Random Forests

Published 2024-01-03 by Kevin Feasel

Norm Matloff answers a reader question:

In my December 22 blog, I first introduced the classic parametric quantile regression (QR) concept. I then showed how one could use the qeML package to perform quantile regression nonparametrically, using the package’s qeKNN function for a k-Nearest Neighbors approach. A reader then asked if this could be applied to random forests (RFs). The answer is yes, and this will be the topic of the current post.

Read on to learn more about how to do this, including some of the challenges you’ll face along the way. H/T R-Bloggers.

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Managing Azure Data Factory IP Ranges for Azure Firewalls

Published 2024-01-03 by Kevin Feasel

Meagan Longoria has a script for us:

While a private endpoint and vNets are preferred, sometimes we need to configure Azure SQL Database or Azure Storage to allow use of public endpoints. In that case, an IP-based firewall is used to prevent traffic from unauthorized locations. But Azure Data Factory’s Azure Integration Runtimes do not have a single static IP. So how do we keep the firewall updated so that ADF can access these resources?

One option is to run everything through a self-hosted integration runtime, which can have a static public IP. But that also means you have to install (and keep updated) a Java SDK if you are converting data to Parquet or ORC files.

Another option is to obtain the IP range list published by Microsoft and update the firewall. That is what I did for a recent project.

Read on to learn more about the latter option, including a Powershell script to do the work.

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Configuring and Troubleshooting SQL Server on Linux

Published 2024-01-03 by Kevin Feasel

I have a new video:

In this video, we will run through detailed configuration recommendations for SQL Server on Linux. We will also find out where you can find your error logs.

This is a mix of show and tell, as I couldn’t show some of the recommendations on my particular hardware. Still, there are a whole boatload of links to additional resources if you want to learn more about why Microsoft chose specific things to show in their SQL Server on Linux performance tuning recommendations.

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Reading and Writing JSON Files in Microsoft Fabric

Published 2024-01-03 by Kevin Feasel

Tom Martens performs two of the three R’s:

JSON is a straightforward, text-based data-interchange format fully independent of any programming language. This simplicity is why JSON documents have become so widespread.

Often the result of querying a REST API is returned as a JSON document, but this is not the only use case why I consider it important to get familiar with reading from and writing to a JSON file. I think a JSON document is ideal for ingesting values into a Python script or any code, values that are helpful to control the logic and behavior of the script. But of course, it’s also easy to store the value of a variable inside the JSON document, where this value will be used when the script is running again.

Click through for some Python code to perform these operations. There are about a dozen other methods you can use as well, but this is one of the best.

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