A Fine Slice Of SQL Server
Boniface Muchendu does a bit of comparison:
Power BI variance measures are essential for delivering actionable insights through dynamic comparisons like month-over-month (MoM) and year-over-year (YoY) performance. This guide shows how to build flexible, filter-aware DAX measures and apply them within clean, user-friendly visuals to enhance your reports.
Click through to see them in action.
Leave a CommentMost teams building production applications understand that “uptime” matters. I am writing this blog to demonstrate how much difference an extra 0.09% makes.
At 99.9% availability, your system can be down for over 43 minutes every month. At 99.99%, that window drops to just over 4 minutes. If your product is critical to business operations, customer workflows, or revenue generation, those 39 extra minutes of downtime each month can be the difference between trust and churn.
Click through for some of the tools and practices that can help get you there in PostgreSQL.
Leave a CommentAdam Bertram runs out of time:
Have you ever had a Pester test hang indefinitely, blocking your entire test suite? Maybe it’s waiting for a network response that never comes, or stuck in an infinite loop. Without proper timeout handling, one bad test can ruin your entire CI/CD pipeline.
In this article, you’ll learn how to implement robust timeout handling for Pester tests using PowerShell runspaces, ensuring your test suite always completes in a predictable timeframe.
Click through for the code and explanation.
Leave a CommentEitan Blumin shares some recommendations:
In many SQL Server environments, DBAs configure SQL Agent jobs with retry attempts (which is a smart move to handle transient issues gracefully). But with it comes a hidden pitfall that often goes unnoticed:
Retry-enabled job steps can silently overwrite logs or cause runaway log growth, depending on how the output file is configured.
The culprit? Improper use of append settings and missing time-based tokens in output file paths.
Read on to learn more.
Leave a CommentTom Collins has troubleshooting tip for us:
Receiving a regular ” There is insufficient system memory in resource pool ‘default’ to run this query” message in the SQL Server Error logs, leading to unavailability of sufficien resources to connect.
This query supplied by Jonathan Kehayias is a great way to start assessing if there is SQL Server memory presure .
Click through for the query and some additional explanation.
Leave a CommentIt’s ten o’clock. Do you know where your Kevin Wilkie is?:
In our last few posts, we’ve looked at how to:
Today, we’re diving into a different, but equally powerful question:
When are my Snowflake warehouses actually running?
Click through for the query and an example of it in action.
Leave a CommentEd Pollack gives us the lowdown:
Columnstore indexes are a powerful tool for storing analytic data directly in SQL Server. This feature has improved in every version of SQL Server since their inception over ten years ago, and SQL Server 2025 is no exception!
The newest enhancements are laser-focused on business continuity and performance. Ordered clustered columnstore indexes, ordered non-clustered columnstore indexes, and database/file shrink operations are all given significant boosts that are worth the time to introduce and learn.
In this article we will dive into each of these changes, how they impact columnstore workloads in SQL Server, and demonstrate their operation.
Read on to see what we’ve got. Nothing in here is ground-breaking, but it’s a set of nice quality of life improvements.
Leave a CommentMarco Russo and Alberto Ferrari compare two keywords:
When you begin modelling in DAX, DISTINCT and VALUES often appear interchangeable: both return the list of unique values for a column in the current filter context. In a clean development model, they behave the same, so it is easy to pick one at random – or worse, swap between them without thinking.
However, they are not identical. The subtle difference is crucial in production models that may one day contain invalid relationships or bad data.
Read on to see how each works and how they differ in practice.
Leave a CommentOscar Martinez lays out the list:
A Power BI Template can mean very different things depending on who you ask. Are we talking about a .PBIT shell, a JSON theme, a turnkey Template App, or merely a thin report wired to a central model?
In this post, we cut through the ambiguity and lay each option side‑by‑side. You’ll learn what’s inside every “template” type and the trade‑offs that matter in real‑world projects—so the next time someone says “just use a template” you’ll know exactly which one fits the bill.
Click through for the post. Also, note that each section is in a drill-through div, so you might accidentally miss some information if you haven’t expanded each topic.
Leave a CommentMiles Cole does a bit of testing:
First, let’s revisit the purpose of the benchmark: The objective is to explore data engineering engines available in Fabric to understand whether Spark with vectorized execution (the Native Execution Engine) should be considered in small data architectures.
Beyond refreshing the benchmark to see if any core findings have changed, I do want to expand in a few areas where I got great feedback from the community:
I really appreciate the approach behind this, both in terms of sticking to more realistic data sizes for many operations as well as performing this test given all of the recent improvements in each engine.
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