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Author: Kevin Feasel

Comparing Write-Back Options for Power BI

Jon Vöge compares two options:

We’ve previously on this blog covered Power Apps write-back for Power BI/Fabric comprehensively, and in the past months we’ve taken a stab at the Fabric Native solution: Translytical Task Flows.

However, when comparing the different options, which solution actually comes out on top?

Read on as Jon contrasts the two options and explains when you might want to use each.

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Modifying Power BI Page Visibility and Active Status via Semantic Link Labs

Meagan Longoria hides (or shows) a page:

Setting page visibility and the active page are often overlooked last steps when publishing a Power BI report. It’s easy to forget the active page since it’s just set to whatever page was open when you last saved the report. But we don’t have to settle for manually checking these things before we deploy to a new workspace (e.g., from dev to prod). If our report is in PBIR format, we can run Fabric notebooks to do this for us.

Click through for a notebook and an explanation.

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SQL Server Auditing Bug Allows Data Exfiltration without Logging

Andreas Wolter describes a bug in SQL Server’s auditing capabilities:

Last week, I was contacted by an IT Leader from Saudi-Arabia who previously found several CVE’s in Oracle and Microsoft SQL Server. He wanted my opinion on a newly discovered security issue in SQL Server Auditing.

Interestingly, his findings directly overlap with a topic I wrote about just last month: Using Data Classification to Audit Data Access.

Emad Al-Mousa identified two vulnerabilities in the SENSITIVE_BATCH_COMPLETED Audit Action Group. Microsoft Security Response Center (MSRC) acknowledged the issue but classified it as low priority – meaning it may not be addressed until a major release, if at all.

Read on to see what the issue is and how you can trigger it today. Andreas also includes a workaround that will work in the meantime.

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Automating SQL Server Deployments via dbatools

David Seis digs into scripted SQL Server installation:

In this and the next two blog posts I will be bringing diverse dbatools commands into scripts that can handle a complete deployment, do a checkup of major health and configuration metrics, and do a true up of a pre-existing instance. This post will cover the complete deployment, which if you have been reading the audit series will be much more than just the SQL install of last post. This time we are aiming for the whole thing. Install, update, configure host, configure SQL, Deploy maintenance. Everything  I can think of!!

Clicking next-next-next one or two times for SQL Server installation is fine—it gives you an idea of what capabilities are available and what you need to know about. By the time you’ve installed SQL Server 5-10 times, you should familiarize yourself with the configuration files (especially because they get auto-generated for you after you use the GUI—SQL Server itself uses these to install!), and should be looking for ways to automate this process and avoid misclicks or wasting time that you could otherwise be using by reading Curated SQL.

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No More Default Semantic Models in Microsoft Fabric

Nicky van Vroenhoven has good news for us:

Another quick post, because today is an important day for everyone working with Fabric and Power BI!

Last month, Microsoft announced they are Sunsetting Default Semantic Models: Yaay! 
Today marks that day: No more automatic child semantic models!

The idea of having a default semantic model seemed like a good one, but the problem was that too many environments had very specific needs that a default semantic model couldn’t anticipate or address. As a result, these tended to confuse end users more than save them time.

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RIP Phil Factor

Tony Davis has some sad news:

We are deeply saddened to share the news that Andrew Clarke, better known to Simple-Talk readers as Phil Factor, recently passed away. He was the site’s editor for several years and continued writing for Redgate long after. Many readers will have learned much of what they know about SQL from Andrew. Others will remember working with him on articles, benefiting from his sharp wit and knowledge, or perhaps meeting him at a PASS conference. To all who knew him, he was a uniquely talented, intelligent, kind, generous, and funny man.

I don’t think I ever met Andrew in person, but I loved his Phil Factor articles. I appreciated all of the work he would put into his testbenches, as well as the irreverent humor he’d sprinkle through. I think my favorite article he ever wrote was this one on the entity-attribute-value anti-pattern in T-SQL and how its siren-like allure drags wave after wave of developers to their doom.

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Installing SQL Server 2025 RC0 on an Azure VM

Koen Verbeeck performs an installation:

I already had a virtual machine in Azure, running SQL Server 2025 CTP 2.0 (which uses a pre-made image). I explain how to set that one up in the article Install SQL Server 2025 Demo Environment in Azure. But I wanted to use the latest preview, which is Release Candidate 0 at the time of writing. Unfortunately, there’s no image available (yet?), so I had to do it the old-school way: installing SQL Server manually.

Read on to see how to do it, as well as a few extra things necessary to make everything work well in Azure.

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Case-Insensitive Search in PostgreSQL

Deepak Mahto performs a search:

Case-insensitive search is one of the most common issues I encounter when helping customers migrate from Oracle or SQL Server to PostgreSQL. Unlike Oracle (with NLS_SORT) or SQL Server (with case-insensitive collations), PostgreSQL does not natively support case-insensitive search.

Read on for a few workarounds, including one that Deepak recommends never using.

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An Introduction to Batch Normalization in Neural Networks

Ivan Palomares Carrascosa shows off one technique for optimizing neural networks:

Deep neural networks have drastically evolved over the years, overcoming common challenges that arise when training these complex models. This evolution has enabled them to solve increasingly difficult problems effectively.

One of the mechanisms that has proven especially influential in the advancement of neural network-based models is batch normalization. This article provides a gentle introduction to this strategy, which has become a standard in many modern architectures, helping to improve model performance by stabilizing training, speeding up convergence, and more.

Read on for a quick description of how it works and a demonstration in Keras.

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