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Author: Kevin Feasel

Self-Intersecting Quadrilaterals in R

Published 2025-05-27 by Kevin Feasel

Jerry Tuttle talks shapes:

A quadrilateral is a polygon having four sides, four angles, and four vertices. A polygon means that the figure is a closed shape, meaning the last line segment connects back to the first one, effectively enclosing an area.

We usually think of quadrilaterals as squares, rectangles, parallelograms, trapezoids, rhombuses, or kites. (I was impressed that my four year-old granddaughter knew the last one, although she called it a diamond!) It could also be irregularly shaped with no name.

However, a polygon may intersect itself. 

Click through for a demonstration of a self-intersecting quadrilateral, including the R code you can use to try it out yourself.

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Performance Testing ZSTD Compression for SQL Server Backups

Published 2025-05-27 by Kevin Feasel

Andy Yun tries out some backup compression:

SQL Server 2025 Public Preview is not even a week old, but I’m impressed with another new capability that was released – a new backup compression algorithm: ZSTD. This one came as a surprise, despite being part of Private Preview, as it was only released with Public Preview.

Click through for Andy’s findings. It’s just one database that is not representative of normal SQL Server databases, but it’s an interesting data point that we can use.

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Combining DISTINCT and UNION

Published 2025-05-27 by Kevin Feasel

Louis Davidson gives it the college try:

When I was perusing my LinkedIn feed the other day, I came across this thread about using SELECT *. In one of the replies, Aaron Cutshall noted that: “Another real performance killer is SELECT DISTINCT especially when combined with UNION. I have a whole list of commonly used hidden performance killers!”

To which started my brain thinking… What does happen when you use these together? And when you use UNION on a set with non-distinct rows, what happens. So for the next few hours I started writing.

Read on for Louis’s findings.

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sqlcmd in SQL Server 2025 and Certificate Chain Not Trusted

Published 2025-05-27 by Kevin Feasel

Vlad Drumea points out a new thing to keep an eye on:

SQL Server 2025 provides ODBC sqlcmd version 17 which enforces an encrypted connection.

If you’re trying to use it to connect to instances that don’t have a CA-signed certificate or where TLS encryption was never properly configured, sqlcmd will throw the famous “certificate chain not trusted” error message:

Sqlcmd: Error: Microsoft ODBC Driver 18 for SQL Server : SSL Provider: The certificate chain was issued by an authority that is not trusted.
Sqlcmd: Error: Microsoft ODBC Driver 18 for SQL Server : Client unable to establish connection.

The proper answer to this is to get trusted certificates. The workaround is what Vlad describes, so click through for that.

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Trying out Microsoft Fabric Data Agents

Published 2025-05-27 by Kevin Feasel

Wolfgang Strasser gives a generative AI solution built into Microsoft Fabric a try:

Today, I wanted to give the new Fabric Data Agents a try. According to the documentation, a Fabric Data Agent is defined as follows:

Data agent in Microsoft Fabric is a new Microsoft Fabric feature that allows you to build your own conversational Q&A systems using generative AI. A Fabric data agent makes data insights more accessible and actionable for everyone in your organization. With a Fabric data agent, your team can have conversations, with plain English-language questions, about the data that your organization stored in Fabric OneLake and then receive relevant answers. This way, even people without technical expertise in AI or a deep understanding of the data structure can receive precise and context-rich answers.

Let’s give it a try and build our first Data Agent.

Click through for the pre-requisites, the setup process, and how everything looked for Wolfgang.

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Building an ML-Friendly Data Lake with Apache Iceberg

Published 2025-05-23 by Kevin Feasel

Anant Kumar designs a data lake:

As companies collect massive amounts of data to fuel their artificial intelligence and machine learning initiatives, finding the right data architecture for storing, managing, and accessing such data is crucial. Traditional data storage practices are likely to fall short to meet the scale, variety, and velocity required by modern AI/ML workflows. Apache Iceberg steps in as a strong open-source table format to build solid and efficient data lakes for AI and ML.

Click through for a primer on Iceberg, how to set up a fairly simple data lake, and some functionality that can help in model training.

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Preventing Injection Attacks in Shiny

Published 2025-05-23 by Kevin Feasel

Arthur Breant shares some advice:

Code injection is a common security vulnerability that involves injecting malicious code into a page or application. This code is then executed, creating the security breach. There are several ways to inject code into an application, and Shiny is unfortunately not immune to these risks.

Click through for a quick overview of the three most common types of injection attack. There’s nothing special about Shiny here—any system that executes code based on user input is potentially vulnerable to injection attacks—so it is good to keep these tips in mind. H/T R-Bloggers.

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Interpreting V$ and GV$ Views in Oracle RAC

Published 2025-05-23 by Kevin Feasel

Kellyn Gorman continues a series on Oracle Real Application Clusters:

Furthering on our Oracle Real Application Clusters (RAC) knowledge, we’re going to go deeper into what we watch for a RAC database that may be different than a single instance.  RAC is built for scale and instance resilience, distributing workloads across multiple nodes.  At the same time, what gives it strength introduces monitoring complexity, especially when you’re not just watching a single instance but multiple, interconnected ones. To manage performance effectively in RAC, you need to understand the difference between V$ and GV$ views, what they show you, and how to interpret cluster-level wait events.  Along with performance, the overall health of the RAC cluster and interconnect must be known, too.

Click through for Kellyn’s explanation.

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Loading JSON into a Microsoft Fabric Eventhouse

Published 2025-05-23 by Kevin Feasel

Christopher Schmidt loads some data:

In the era of big data, efficiently parsing and analyzing JSON data is critical for gaining actionable insights. Leveraging Kusto, a powerful query engine developed by Microsoft, enhances the efficiency of handling JSON data, making it simpler and faster to derive meaningful patterns and trends. Perhaps more importantly, Kusto’s ability to easily parse simple or nested JSON makes it easier then ever to extract meaningful insights from this data. The purpose of this blog post is to walk through ways that JSON data can be loaded into Eventhouse in Microsoft Fabric, where you can then leverage Kusto’s powerful capabilities for this. I’ve tried this a few different ways, and the below approach is the fastest, most efficient low-code way to ingest the data into the Eventhouse. As JSON inherently supports different schemas in a single file, the expectation here is that we have a json file with varying schemas within a single file, and we would like to load this into our Eventhouse for efficient parsing with KQL.

Read on for the process.

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Optional Parameter Plan Optimization in SQL Server 2025

Published 2025-05-23 by Kevin Feasel

Brent Ozar is down with OPP(O):

SQL Server 2025 improved PSPO to handle multiple predicates that might have parameter sensitivity, and that’s great! I love it when Microsoft ships a v1 feature, and then gradually iterates over to make it better. Adaptive Memory Grants were a similar investment that got improved over time, and today they’re fantastic.

SQL Server 2025 introduces another feature to mitigate parameter sniffing problems: Optional Parameter Plan Optimization (OPPO). It ain’t perfect today – in fact, it’s pretty doggone limited, like PSPO was when it first shipped, but I have hopes that SQL Server vNext will make it actually usable. Let’s discuss what we’ve got today first.

Okay, I really had to stretch the truth to make my lead-in work, but I’m too proud of it to change anything. Click through to see where OPPO is today. Even with just one optional parameter working well, there is still a class of stored procedures that this can help: the “get by one ID, or get me all of them” type.

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