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Curated SQL Posts

Sharing Data between Shiny Modules

Published 2026-03-22 by Kevin Feasel

Colin Fay explains an architecture:

Some people have recently been vocal about misuses of the "stratégie du petit r", a mechanism for sharing data across {shiny} modules that was detailed both in the Engineering Production-Grade Shiny Apps book and in an older post written in 2019 on this blog.
And yes, if you’re wondering, I did feel old when I realized this blog post is almost 7 years old now

I’m always happy to be proven wrong, to challenge the way I build software, and to become a better software engineer. But given that we weren’t contacted to discuss the ideas behind this strategy, I thought the moment was perfect to give y’all an update on the latest approaches I’ve been using to share data across {shiny} modules, along with some thoughts and comments on the "stratégie du petit r".

Click through to learn more about Shiny modules, the challenge of passing information between modules, and some high-level ideas of how to pass information between these modules without everything falling apart. H/T R-Bloggers.

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Azure SQL Managed Instances and CPU

Published 2026-03-20 by Kevin Feasel

Joe Obbish answers a question:

I’m going to open with a perhaps controversial statement: “when you buy 4 vCores on the Azure SQL Managed Instance platform, what you’re actually buying is 2 physical cores presented as 4 hyperthreaded cores to SQL Server”. That means that if you have 8 physical cores on your SQL Server machine today then your starting Managed Instance vCore equivalent count could be closer to 16 vCores instead of 8. Perhaps this is already well known to everyone else, but I couldn’t find any (accurate) writing on this topic so I gave it a shot.

Click through for a series of tests that do not look great for SQL Managed Instances. And it doesn’t even have to do with storage this time. Azure SQL Managed Instance has to be one of the most disappointing Azure products, simply on hardware grounds alone.

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What’s New in SQL Database for Fabric

Published 2026-03-20 by Kevin Feasel

Idris Motiwala makes some announcements:

The new Migration Assistant for SQL databases simplify moving SQL Server and Azure SQL workloads into Fabric. Designed for SQL developers, it imports schema via DACPACs, identifies compatibility issues, and provides clear, actionable guidance before migration. Built-in assessment and data copy workflows help teams move from evaluation to cutover with less manual effort, preserving existing SQL skills while accelerating time to value on Fabric’s unified analytics platform.  Ready to simplify your SQL migration journey? We will begin rolling this out in the coming weeks, and it will soon be accessible through the Fabric portal.

Click through for more things that are currently in place, including several items that are now GA.

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Prevent Future Date Spillage in Power BI Visuals

Published 2026-03-20 by Kevin Feasel

Kenneth Omorodion lives in the now:

For Power BI developers, one very common (and frustrating) issue is when measures spill into future dates on charts especially when working with some time intelligence DAX calculations (e.g. MTD, YTD, etc.), date dimensions that extend beyond current date, and forecast-enabled tables.

In Power BI charts (e.g. line or bar charts), apart from dates with data, measures are also evaluated for every date on the axis, regardless if there is data or not. For example, if my dates table runs to 2026 December, but my data table only have data up to today, when I create a measure that leverages MTD or YTD for example, Power BI will tend to evaluate the measure for all dates that exist in my Dates table, unless I explicitly apply a logic to prevent this behaviour. This behaviour might result in flat lines on charts, misleading trends, and confusion to intended users.

In this article, I will demonstrate some examples of approaches to prevent or manage future dates spillage in Power BI.

Click through for some tips.

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Group Managed Service Accounts and Failover Cluster Instances

Published 2026-03-20 by Kevin Feasel

Deepthi Goguri covers the real value of gMSA accounts:

A Failover Cluster Instance (FCI) is a SQL Server high-availability solution where multiple servers share the same storage. If the active node fails, SQL Server automatically fails over to another node with minimal downtime.

You need to follow all the steps mentioned in the first blog post, which you can find here.

From there, Deepthi covers how to change the SQL Server service account to use a gMSA if you have FCI or other high availability setups for SQL Server.

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What’s New in OneLake

Published 2026-03-20 by Kevin Feasel

Josh Caplan provides an update:

With shortcuts and mirroring in OneLake, you get zero-copy, zero-ETL capabilities to connect your multi-cloud data estate. Whether your data sits in Azure, AWS, Google Cloud, or Oracle, on-premises, or across platforms like SAP, Dataverse, Snowflake, and Azure Databricks, you can connect it to OneLake without data movement or duplication. No more sprawling ETL pipelines. No more out-of-date copies. No more data silos.

Today, we’re expanding mirroring to now include SharePoint lists (Preview) and adding mirroring via shortcuts for Azure Monitor and Dremio (Preview). We are also releasing mirroring for Oracle and SAP Datasphere into general availability. Beyond these core mirroring capabilities, we are now introducing extended capabilities in mirroring designed to help you operationalize mirrored sources at scale. These capabilities include Change Data Feed (CDF) and the ability to create views on top of mirrored data, starting with Snowflake and will be offered as a paid option.

Click through for more of what came out of FabCon.

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Creating a Power BI Semantic Model Online

Published 2026-03-19 by Kevin Feasel

Gilbert Quevauvilliers doesn’t need Power BI Desktop:

It has been in the service for quite a while so I thought I would blog about it in terms of how you can create a power BI semantic model simply using the web interface. This means you no longer need Power BI desktop, or a Windows PC to get going.

This is quite a significant change because at times you need a lot of resources on your Windows PC or you’re working on a Mac and could not do this previously.

So, I will give an overview below on how you can create the semantic model just by using your browser.

Click through to see how.

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Materialized Lake Views now GA in Microsoft Fabric

Published 2026-03-19 by Kevin Feasel

Balaji Sankaran makes an announcement:

Since introducing MLVs (Preview) at Build 2025, data engineers have used them to replace hand-built ETL pipelines with a few declarative Spark SQL statements, and their feedback directly shaped this release.

This update closes the most important gaps since reaching preview and makes MLVs production-ready at scale. With multi-schedule support, broader incremental refresh, PySpark authoring, in-place updates, and stronger data quality controls, teams can now build, run, and evolve medallion pipelines with far less operational overhead.

Click through to see what’s changed since the preview.

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Configuring a New Windows Device via Chocolatey

Published 2026-03-19 by Kevin Feasel

Reitse Eskens writes a script:

Last month, my company got me a new laptop. I have very little choice in which one I get, and I have to reinstall a lot of software. So, what to do next?

Click through for an example of such an installation script. Having one or more Chocolatey scripts does help, especially for fairly stable corporate machines. I’ve found it to be moderately useful for home devices, where I tend to cycle through software a bit more frequently. But it does make the process of rebuilding a Windows-based machine less painful.

On the Linux side, this is more built-in. For anything via repo managers (such as apt or yum), you can list your installed packages and turn that into a script. It’s a similar story for snap or flatpak packages, though maybe a little bit less conveneint. For AppImage files or deb/rpm files you installed separately from the repo, it’s a bit more of a mixed bag.

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Idempotence in SQL Scripts

Published 2026-03-19 by Kevin Feasel

Jared Westover lays out some solid advice:

Imagine you’ve spent weeks preparing a software release for production, only to deploy it late one night and receive an error that the table or column already exists. This occurs in production environments, even when you use migration-based deployment methods such as DbUp. How can you ensure or at least reduce the likelihood of an error like this in the future?

At a prior job, we needed to write idempotent scripts because the deploy-to-QA process would run every script for the sprint every time someone checked in a new script. This prevented a few classes of release error, and I’ve carried that practice with me to future engagements.

SQL Server 2016 then gave us several helpers like CREATE OR ALTER for stored procedures and views, or DROP IF EXISTS for views and tables. It’s not a complete set of everything I’d like the functionality to do, but it’s a lot more convenient than what we had to do in prior versions.

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