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Author: Kevin Feasel

Azure Tenants and Microsoft Fabric

Published 2025-11-05 by Kevin Feasel

Andy Cutler begins a new series on Microsoft Fabric architecture:

Our Fabric Architecture journey starts with Azure Tenants (the kick-off blog in this series is here with a few jumping-off links to get started with thinking about Fabric Architecture). If you’re ready to spent time sketching out your Fabric Capacity planning, workspace strategy, domain topology, lakehouse/warehouse creation, data loading processes…you might want to stop for a minute and think about tenants. The question I’d like you to consider is “What do I need to know when working with a single or a multi-tenancy approach?” Let’s unpack this question because while it might sound like a simple list, it actually shapes your governance, scalability, and Fabric operational model. If you’re a seasoned Azure Architect veteran then you already know how to decide between single and multi-tenant cloud rollouts (also, please comment if you have anything to add please), if you work with Fabric/Data and don’t really dive into Azure architecture on a daily basis then please stick around. Hopefully this blog gets you thinking about single/multi-tenant architectures and the benefits/costs.

Read on for a dive into what tenants are, the benefits of single- versus multi-tenancy, and how it all ties into Fabric.

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Working with JSON Indexes in SQL Server 2025

Published 2025-11-05 by Kevin Feasel

Koen Verbeeck tries out a new index type:

We’re trying the new JSON data type in SQL Server for data stored as JSON in a table. When we query it using functions such as JSON_VALUE, we see a full table scan is performed for each query. Is there a way we can index the JSON to improve performance?

The JSON index has a somewhat different definition of its structure and there are some limitations to how it works, but for specific JSON-related queries, you can see the improvement.

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Function Volatility and PostgreSQL Partition Performance

Published 2025-11-05 by Kevin Feasel

Deepak Mahto covers how function volatility can affect how queries on partitioned data perform:

In one of our earlier blogs, we explored how improper volatility settings in PL/pgSQL functions — namely using IMMUTABLE, STABLE, or VOLATILE — can lead to unexpected behavior and performance issues during migrations.

Today, let’s revisit that topic from a slightly different lens. This time, we’re not talking about your user-defined functions, but the ones PostgreSQL itself provides — and how their volatility can quietly shape your query performance, especially when you’re using partitioned tables.

Click through for one example using date-based partitioning and date functions.

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Calling Logic Apps via Data Factory Pipelines

Published 2025-11-05 by Kevin Feasel

Andy Brownsword flips the script:

Last week we looked at calling a Data Factory Pipeline from a Logic App. This week I thought we’d balance it out by taking a look at calling a Logic App from an Azure Data Factory (ADF) Pipeline.

When building the Logic App last week we had to create our own polling mechanism to check for completion of the pipeline. The process is much simpler in the opposite direction. I specifically want to highlight two approaches, and save some pennies whilst we’re at it.

I am all about saving pennies, so be sure to check out that section as well.

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Consider What You Count

Published 2025-11-04 by Kevin Feasel

Hans-Jürgen Schönig ran out of fingers:

The purpose of this post is not to share some fancy SQL techniques, some super cool magic AI tool that does whatever new hot thing or some crazy coding technique one can use in PostgreSQL. This is all about two simple things that are often forgotten or just neglected. We are of course talking about “awareness” and “clarity”.

Yes, it is that simple. If you don’t know what you have or if you don’t know what you want, all data is worthless (in a best case scenario) or even dangerous. 

The concept for discussion here is a simple count: how many customers do you have? But even fairly simple questions like this can cause difficulty in answering because of business complexities that we need to model in our databases. The problem is that the expedient answer may not be the correct one. This is also a key reason why we end up with debates in meetings about which value of revenue to use.

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Creating Your Own ggplot2 Geom

Published 2025-11-04 by Kevin Feasel

Isabella Velasquez is feeling creative:

If you use ggplot2, you are probably used to creating plots with geom_line() and geom_point(). You may also have ventured into to the broader ggplot2 ecosystem to use geoms like geom_density_ridges() from ggridges or geom_signif() from ggsignif. But have you ever wondered how these extensions were created? Where did the authors figure out how to create a new geom? And, if the plot of your dreams doesn’t exist, how would you make your own?

Enter the exciting world of creating your own ggplot2 extensions.

The post looks a lot like a series of slides, and it takes you through the process of creating a new geom. H/T R-Bloggers.

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An Error with Log Shipping in an Contained Availability Group

Published 2025-11-04 by Kevin Feasel

Rich Benner diagnoses an issue:

We’ve recently come up across a fun little bug with log shipping in contained availability groups. A cursory Google search did not bring up any useful posts for troubleshooting, so here we are. If you’re here for the bug, feel free to scroll to the bottom of this post. For the rest of you, however, let’s add a little context to this scenario before getting to the meat of the situation.

Click through for the issue, as well as how Rich was able to resolve the problem.

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Finding the Top 10 Products each Year with DAX

Published 2025-11-04 by Kevin Feasel

Marco Russo and Alberto Ferrari build a top ten list:

We have written and updated a few pieces in the past about how to find the top products, such as Filtering the top products alongside the other products in Power BI and Filtering the Top 3 products for each category in Power BI.

Generally speaking, finding the top products requires using GENERATE and TOPN. However, there is an interesting variation of this scenario that solves a specific business problem. Once we have determined the top 10 products by year, we want to filter only those that appear in the top 10 in most years. Obtaining that list of products helps identify evergreen products, that is, the products that remain in the best-seller list consistently.

Click through for the demonstration.

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