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Author: Kevin Feasel

Logging in PostgreSQL

Published 2025-08-21 by Kevin Feasel

Elizabeth Christensen saves some information:

A modern-day Postgres instance creates robust and comprehensive logs for nearly every facet of database and query behavior. While Postgres logs are the go-to place for finding and debugging critical errors, they are also a key tool in application performance monitoring.

Today let’s get set up with logging for Postgres – starting with the basics of what to log, how to log what you want, and as reward for your hard work – how to use these to monitor and improve performance. The Postgres docs on logs are excellent, so please consult those for the most up to date and comprehensive configurations. This blog reads between the lines a bit beyond the docs to offer some practical advice and settings. As always, your mileage may vary.

Click through for several tips and a lot of information on the topic of logging.

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A Primer on Bayesian Modeling

Published 2025-08-20 by Kevin Feasel

Hristo Hristov is speaking my language:

Multivariate analysis in data science is a type of analysis that tackles multiple input/predictor and output/predicted variables. This tip explores the problem of predicting air pollution measured in particulate matter (PM) concentration based on ambient temperature, humidity, and pressure using a Bayesian Model.

Click through for a detailed code sample and explanation.

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Using Dropdowns in Translytical Task Flows

Published 2025-08-20 by Kevin Feasel

Jon Vöge isn’t content with slicers:

When building Translytical Task Flows for Power BI / Fabric, you’ll quickly come head to head with a few limitations:

  • They won’t work in Embedded scenarios
  • They won’t work with PBIR and PBIP formats
  • They natively only support inputs coming from the three new slicer types: Text, Button and List Slicers (all of which are in preview at the time of writing).

Read on to see how you can partially get around the third limitation.

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A Primer on Join Operators

Published 2025-08-20 by Kevin Feasel

Andy Brownsword takes a peek at the three most common types of join operators, plus a bonus:

When reviewing our execution plans we’ll see joins executed using different operators. The type of operator is chosen based on the data that’s available to join and how the optimiser wants to execute it.

In this post we’ll take a look at what the operators are, when they are used, and how they work. These are the operators we’ll cover:

  • Nested Loop Joins
  • Merge Joins
  • Hash Match Joins
  • (Bonus) Adaptive Joins

Read on for a quick overview of which works best when.

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T-SQL Tuesday 189 Round-Up

Published 2025-08-20 by Kevin Feasel

Taiob Ali summarizes this month’s T-SQL Tuesday:

I would like to thank all the participants of T-SQL Tuesday #189. If I missed your post, it was not intentional. Please let me know, and I will add it to this list.

I am proud of this community and feel lucky to be a small part of it. I admire everyone who joined the blog party and shared their thoughts on how AI is changing our careers, as well as your thoughts on AI tools.

Click through to see the responses.

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Using the Tabular Object Model via Semantic Link Labs

Published 2025-08-20 by Kevin Feasel

Gilbert Quevauvilliers does a bit of connecting:

In this blog post I am going to show you how to use the powerful Semantic Link Labs library for Tabular Object Model (TOM) for semantic model manipulation.

The goal of this blog post is to give you an understanding of how to connect using TOM, then based on the documentation use one of the functions.

Don’t get me wrong the documentation is great, but when implementing it, it works a little differently and I want others to know how to use it, so it can automate and simplify some repetitive tasks.

Read on for the instructions and some of the things you can do with the Semantic Link Labs library in Microsoft Fabric.

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Ordered Insert Optimization in OrioleDB

Published 2025-08-20 by Kevin Feasel

Alexander Korotkov deals with hot page issues:

When many sessions try to insert into the same B-tree leaf page, classic exclusive page locking serializes progress and wastes time on sleep/wake cycles. We’re introducing a batch page insertion path that lets the session holding the page lock insert for itself and its neighbors. The result: dramatically reduced lock waits, and big gains at high client counts (2X throughput boost starting from 64 clients in our benchmark).

Click through to see how it differs from normal PostgreSQL behavior on what is one of the more common performance problems on busy OLTP systems.

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Merging Data Tables in Power Query

Published 2025-08-20 by Kevin Feasel

Reza Rad merges tables together:

Power Query gives you the capability of joining (merging) tables together using key columns, and there are multiple types of Merge types to choose from. There is another way to merge tables especially when the data source is a relational database system. In this article and video, You will learn how this options works.

Click through for the article and accompanying video.

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Auditing in Oracle

Published 2025-08-19 by Kevin Feasel

David Fitzjarrell digs into the options available for security audits in Oracle:

Security rears its ugly head yet again, this time with security audits. Depending upon the RDBMS auditing can be a breeze or a nightmare. Oracle has done wonders in making this task simpler for the DBA to enable, configure and maintain. Let’s take a dive into this topic and shed some light on it.

By default when Oracle is installed the newest foray into auditing, the Unified Audit Trail, is disabled. In releases prior to 12 basic auditing was automatically available, but it wrote to possibly three locations:

Read on for those three locations, how things have changed with Unified Audit Trail, and some of the information you can get back from it.

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Visualizing ML Model Outcomes with Matplotlib

Published 2025-08-19 by Kevin Feasel

Matthew Mayo shares a few tips:

Visualizing model performance is an essential piece of the machine learning workflow puzzle. While many practitioners can create basic plots, elevating these from simple charts to insightful, elevated visualizations that can help easily tell the story of your machine leanring model’s interpretations and predictions is a skill that sets great professionals apart. The Matplotlib library, the foundational plotting tool in the scientific and computational Python ecosystem, is packed with features that can help you achieve this.

This tutorial provides 7 practical Matplotlib tricks that will help you better understand, evaluate, and present your machine learning models. We’ll move beyond the default settings to create visualizations that are not only aesthetically pleasing but also rich in information. These techniques are designed to integrate smoothly into your workflow with libraries like NumPy and Scikit-learn.

Click through for those tips.

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