Press "Enter" to skip to content

Author: Kevin Feasel

Using the trap Statement in Powershell

Published 2025-11-11 by Kevin Feasel

Patrick Gruenauer plays his trap card:

In PowerShell, error handling is a crucial aspect of writing robust and reliable scripts. One of the powerful features to manage errors gracefully is the trap statement. The trap statement allows you to define a block of code that runs whenever a terminating error occurs in your script. This can be particularly useful for logging errors, cleaning up resources. Let’s dive in.

Read on to see how it works. My preference would be to use try/catch blocks instead of trap statements, but it is interesting to see it there.

Leave a Comment

Microsoft Fabric Warehouse Snapshots now GA

Published 2025-11-11 by Kevin Feasel

Twinkle Cyril makes an announcement:

Managing data consistency during ETL has always been a challenge for our customers. Dashboards break, KPIs fluctuate, and compliance audits become painful when reporting hits ‘half-loaded’ data. With Warehouse Snapshots, Microsoft Fabric solves this by giving you a stable, read-only view of your warehouse at a specific point in time and now, this capability is Generally Available! Think of this as a true time travel database, an industry-first capability that sets us apart.

I wonder how much they differ from the database snapshots available in SQL Server.

Leave a Comment

Index Skip Scans in PostgreSQL 18

Published 2025-11-11 by Kevin Feasel

Hans-Jürgen Schönig demonstrates a new capability in PostgreSQL:

PostgreSQL 18 brings a couple of performance related features to the table which will help applications to run more efficiently, providing a better and more enjoyable user experience. One of those performance features is called “skip scans”. Most of you might ask yourself at this point: Wow, sounds cool, but what is a skip scan? The purpose of this post is to shed some light and explain how this works, what it does and most importantly: How one can benefit from this feature in real life.

Click through for the demo.

Leave a Comment

Fuzzy Text Match in SQL Server

Published 2025-11-11 by Kevin Feasel

Rob Farley is excited:

However, SQL Server 2025 does bring some great options for doing fuzzy string matches, making custom Data Quality options even richer. I’ve spoken about this at some user groups recently (including tomorrow, remotely for TriPASS, and in a few weeks in Melbourne and Sydney for Difinity), and in that session I go much deeper into how I see data matching going. I’ll also write more about these methods in future posts, but it’ll take a few posts, covering quite a few sub-topics.

If you want to see that session, our user group (the Triangle Area SQL Server Users Group) is hosting it Wednesday morning Australia time, or this evening US Eastern Standard Time.

Leave a Comment

Database Scoped Configurations in SSMS

Published 2025-11-11 by Kevin Feasel

Greg Low takes a peek at something fairly new to SQL Server Management Studio:

But users were only an early part of this story. Whether you want users contained or not, there are so many configurations at the server level that people wanted to be able to configure at each database, instead of only at the server. So, we saw the introduction of Database Scoped Configurations.

What was missing from this was a UI for setting them. 

Read on to see how that has changed.

Leave a Comment

Pulling Random Values from a Gaussian Distribution in T-SQL

Published 2025-11-10 by Kevin Feasel

Sebastiao Pereira has another way of populating a random variable:

Generating random numbers from a normal distribution is essential for accuracy and realistic modeling. Used for simulation, inference, cryptography, and algorithm design for scientific, engineering, statistical, and AI domains. Is it possible to create random Gaussian numbers in SQL Server using the Ziggurat algorithm without external tools?

I was not familiar with this technique, so it’s neat to see it in action.

Leave a Comment

Pandas vs Polars for DataFrame Modification

Published 2025-11-10 by Kevin Feasel

Russ Hyde compares Pandas and Polars:

In Data Science we are often working with rectangular data structures – databases, spreadsheets, data-frames. Within Python alone, there are multiple ways to work with this type of data, and your choice is constrained by data volume, storage, fluency and so on. For datasets that could readily be held in memory on a single computer, the standard Python tool for rectangling is Pandas, which became an open-source project in 2009. Many other tools now exist though. In particular, the Polars library has become extremely popular in Python over recent years. But when Pandas works, is well-supported, and is the standard tool in your team or your domain, and if you are primarily working with in-memory datasets, is there a value in learning a new data-wrangling tool? Of course there is.

Read on for a demonstration of fairly basic data operations and how they differ in Pandas vs Polars.

Leave a Comment

An Overview of Azure Managed Cassandra’s Architecture

Published 2025-11-10 by Kevin Feasel

Amy Abel describes an architecture:

I’ve been learning about Azure Managed Cassandra recently, and it’s very different from the usual relational SQL Server database. The documentation and tutorials can seem confusing at first, but once I broke things down it was easier to understand basic concepts.

Read on for a warning about different flavors of Cassandra, as well as how Microsoft has organized things in their implementation of Cassandra.

Leave a Comment

An Overview of PostgreSQL Internals

Published 2025-11-10 by Kevin Feasel

Elizabeth Christensen shows some of the ways to view internal information in PostgreSQL:

Postgres has an awesome amount of data collected in its own internal tables. Postgres hackers know all about this  – but software developers and folks working with day to day Postgres tasks often miss out the good stuff.

The Postgres catalog is how Postgres keeps track of itself. Of course, Postgres would do this in a relational database with its own schema. Throughout the years several nice features have been added to the internal tables like psql tools and views that make navigating Postgres’ internal tables even easier.

Today I want to walk through some of the most important Postgres internal data catalog details. What they are, what is in them, and how they might help you understand more about what is happening inside your database.

Click through for an overview of catalog tables and catalog views (similar to SQL Server’s system tables and Dynamic Management Views, respectively).

Leave a Comment