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Month: May 2025

New Capabilities in SQL Server 2025 CTP 2.0

Published 2025-05-20 by Kevin Feasel

Randolph West lays out some favorite features:

Three years ago, when the first public preview of SQL Server 2022 (CTP 2.0) was announced, I was a few months in at the SQL Docs team, and had very little to do with that release.

Three years later, the team is slightly larger (we’re called Data Docs now), and I was much more involved with helping scores of people merge the content for SQL Server 2025 (CTP 2.0).

Click through for Randolph’s favorite features for administrators and for developers that are available right now in the community technical preview.

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The Spurious Correlations R Package

Published 2025-05-19 by Kevin Feasel

Mauricio Vargas S. shows correlation:

spuriouscorrelations package started as a fun project for one of my tutorials.

Here is a case of an interesting correlation: the number of people who drowned by falling into a pool and the number of films Nicholas Cage appeared in.

Click through for examples and how to use the package. If you’re interested in more of these, Tyler Vigen’s website has plenty, and he even wrote a book. H/T R-Bloggers.

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Backfilling Data in TimescaleDB

Published 2025-05-19 by Kevin Feasel

Semab Tariq takes us through a problem:

Backfilling data into a TimescaleDB hypertable in production can be very tricky, especially when automated processes like compression policies are involved. From past experience, we have seen that if backfill operations aren’t handled properly, they can interfere with these automated tasks, sometimes causing them to stop working altogether. 

This blog covers a safer and more reliable approach to backfilling hypertables, along with best practices to prevent disruptions to compression and other background processes.

Read on for several tips. Backfills can be challenging in any database, but time-scale databases like TimescaleDB introduce their own unique issues.

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Methods to Expand a Power BI Matrix Visual

Published 2025-05-19 by Kevin Feasel

Chris Webb runs some performance tests:

If you have a Power BI report with a matrix visual on it it’s quite likely that you’ll want all the levels in the matrix to be fully expanded by default. But did you know that the way you expand all the levels could have performance implications, especially if you’re using DirectQuery mode? Here’s an example.

Click through to see what options are available to you and their performance implications.

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Thoughts on Index Rebuilds

Published 2025-05-19 by Kevin Feasel

Kevin Hill shares some thoughts:

Here’s the truth: if you’re doing this daily on indexes smaller than 10,000 pages, you might be chewing up CPU, bloating your logs, and annoying your users  for zero gain.

Let’s fix that.

I disagree with Kevin on index reorganization, in that I would never perform index reorgs—there’s no there there. Even so, I firmly agree with the thrust of Kevin’s argument and believe that, in the majority of cases, companies with DBAs (or people who have stumbled through maintenance plans before) are maintaining indexes too much rather than not enough.

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Aesthetics and Usability as Complements

Published 2025-05-19 by Kevin Feasel

Elena Drakulevska explains that you can’t succeed without both:

There’s this UX law called the Aesthetic-Usability Effect. And it basically says:

People will lean more toward better-looking products—even if they’re not the best-performing ones.

Sounds wild, right? But it’s true. A gorgeous report might win hearts even if a more useful, but less attractive one, is sitting in the corner gathering dust.

And honestly? I agree… to a point.

Read on to see when it falls apart and why you can’t focus solely on one or the other. Utility curves are convex, after all.

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Checking Valid Configuration Items for Azure SQL Managed Instances

Published 2025-05-19 by Kevin Feasel

Ben Johnston looks at the art of the possible:

In my last post I described issues that might stop your migration to a SQL Server Managed Instance (SQL MI). This covers configuration items that differ or are not supported in SQL MI. These likely won’t stop your migration, but they could slow you down if you aren’t ready for these changes.

As with previous issues discussed, testing your migration is key. Validate all of your settings and be prepared to make some changes during your migration process. Most of the incompatible options make sense when you think about the purpose of SQL MI – it is controlled by Microsoft. Hardware settings, local file access, high-availability settings, and auditing are configured differently or completely disabled.

Click through to see what you can and cannot do when it comes to configuration.

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Extending caret for Spatial Machine Learning

Published 2025-05-15 by Kevin Feasel

Jan Linnenbrink looks at spatial data:

This document shows the application of caret for spatial modelling at the example of predicting air temperature in Spain. Hereby, we use measurements of air temperature available only at specific locations in Spain to create a spatially continuous map of air temperature. Therefore, machine-learning models are trained to learn the relationship between spatially continuous predictors and air temperature.

When using machine-learning methods with spatial data, we need to take care of, e.g., spatial autocorrelation, as well as extrapolation when predicting to regions that are far away from the training data. To deal with these issues, several methods have been developed. In this document, we will show how to combine the machine-learning workflow of caret with packages designed to deal with machine-learning with spatial data. Hereby, we use blockCV::cv_spatial() and CAST::knndm() for spatial cross-validation, and CAST::aoa() to mask areas of extrapolation. We use sf and terra for processing vector and raster data, respectively.

Click through to see how it all works. H/T R-Bloggers.

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Setting up Physical Streaming Replication in PostgreSQL

Published 2025-05-15 by Kevin Feasel

Umair Shahid pushes the contents of the write-ahead log to another machine:

Physical streaming replication in PostgreSQL allows you to maintain a live copy of your database on a standby server, which continuously receives updates from the primary server’s WAL (Write-Ahead Log). This standby (or hot standby) can handle read-only queries and be quickly promoted to primary in case of failover, providing high availability and disaster recovery. 

In this guide, I will walk through provisioning a primary PostgreSQL 16 server and a standby server on Linux, configuring them for streaming replication, and verifying that everything works. I assume you are an experienced engineer familiar with Linux, but new to PostgreSQL replication, so I will keep it friendly and straightforward.

Click through for the process.

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