Erik Darling takes an academic concept and explains what it means in practice for SQL Server. Erik does a good job describing the concepts of atomicity, consistency, isolation, and durability. I do agree with Erik’s take on consistency, which tends to be the property that database platforms minimize in return for scalability. The descriptions of all four are good, though Erik has a lot more content that digs into consistency and isolation.
Leave a CommentAuthor: Kevin Feasel
Sairam Yeturi reduces ETL and ELT requirements:
Organizations often face challenges when trying to scale analytics across large volumes of data stored in centralized SQL databases. As business teams demand faster, more tailored insights, traditional reporting pipelines can become bottlenecks. By adopting Lakehouse architecture with Microsoft Fabric, business groups can mirror their SQL data into OneLake and organize it using the Medallion architecture—Bronze, Silver, and Gold layers. Materialized lake views play a crucial role in this setup, enabling automated, declarative transformations that clean and enrich data in the Silver layer. This empowers teams to build reliable dashboards and AI-driven insights on top of curated data, all while maintaining performance, governance, and security on a scale.
In this post, we will cover how enterprises can use materialized lake views to streamline data orchestration and enhance data quality, monitoring across silver and gold layers, while mirroring their SQL DB tables to Fabric in the Bronze layer.
The best use case for this is a scenario in which your underlying data is already essentially in a star schema or at least easily transformable into one, and you have no interest in modifying the data in the view directly. Do read the limitations before digging in, though, as there are some big ones.
Leave a CommentRick Dobson wants a measure of variation:
This tip presents a brief introduction to Common Table Expressions (CTE), along with a few references for those seeking additional details on CTEs beyond those described and demonstrated here. We will examine CTEs that are defined by either one or two SELECT statements. Additionally, we will provide a demonstration of a recursive CTE. All the examples illustrate how to process time series datasets with CTEs.
Click through for the tip.
Leave a CommentJayita Gulati compares a few mechanisms to classify data:
When you have a small dataset, choosing the right machine learning model can make a big difference. Three popular options are logistic regression, support vector machines (SVMs), and random forests. Each one has its strengths and weaknesses. Logistic regression is easy to understand and quick to train, SVMs are great for finding clear decision boundaries, and random forests are good at handling complex patterns, but the best choice often depends on the size and nature of your data.
In this article, we’ll compare these three methods and see which one tends to work best for smaller datasets.
All three are quite reasonable algorithms to compare, though I’d want to add in gradient descent or XGBoost, as I’d expect it to perform better than random forest with small datasets.
Leave a CommentConnie Xu makes an announcement:
We’re thrilled to announce that the Copy job Activity is now in Preview!
This new orchestration activity brings the simplicity of the Copy job item directly into your Microsoft Fabric Data Factory pipelines, enabling you to manage data movement alongside transformations, notifications, and more; all in one place.
Read on to learn more about it, including how it differs from the Copy activity and the Copy job item.
Leave a CommentJon Vöge sends along some data:
A common misconception about Translytical Task Flows is that the only way for you to parameterize and pass user inputs to the User Data Function, is through Slicers in Power BI.
That is not true at all.
In fact, one of the most powerful ways of integrating Task Flows into your Power BI reports, is by allowing user selections made in visualisations in your report, flow through to your task flow.
Read on to see how you can do this.
Leave a CommentAndy Brownsword takes a closer look at the big three join operators in SQL Server:
In the previous post I explained Join Operators in SQL Server. Whilst compiling that I dug a little deeper and came across a few interesting points I thought were worth sharing.
Let’s look at behaviour of the operators which may occur under specific conditions. Hopefully you find them as interesting as I did:
Click through for an interesting tidbit about nested loops joins, merge joins, and hash joins.
Leave a CommentBoniface Muchendu wants to avoid DAX for this calculation:
Power BI visual calculations provide a simplified way to perform time-based comparisons like week-over-week (WoW) analysis without writing complex DAX. Power BI’s time intelligence functions are powerful, but they can become challenging when working with custom calendars or unique business logic. If you’ve ever struggled with calculating WoW performance, this post will show you how to streamline the process using visual calculations.
Read on to see how. Though one thing to keep in mind is that it says week-over-week, but that assumes you have data for each week. I don’t believe this solution would cleanly handle gaps in data, so that would be something you’d want to do beforehand.
Leave a CommentPriya Sathy has an announcement:
The first release candidate (RC0) of SQL Server 2025 is now available. As we move toward general availability, our focus shifts to delivering enhanced stability, performance, and product improvements based on your feedback.
There are a few new features that Priya specifies in the post, so check those out. This also puts us on track for a GA delivery right around conference season.
Leave a CommentAnshika Varshney takes us through some secrets management:
Secret management is a critical part of working with Kubernetes. When working with Kubernetes, you’ll often need to store and use sensitive information — like database passwords, API keys, and certificates. That’s where Kubernetes Secrets come in. But here’s the thing: while Kubernetes provides a way to store secrets, using them incorrectly can still expose your sensitive data.
This blog will walk you through:
- What Kubernetes Secrets are
- How they work
- Common mistakes (the wrong way)
- Best practices (the right way)
- Real-life examples and configurations
Read on for the full article. I do want to iterate what I consider the most important of Anshika’s points: these things should never go into source control. The problem is, source control history is a lot more permanent than people customarily think about, so if you accidentally checked in a password in a config file somewhere, then got rid of the line with the password in it, a bad person with access to your source control can still look at history and find that password. The mechanism to rewrite Git history to remove a line’s existence is purposefully difficult, so a lot of developers don’t even know that it’s possible. But even then, if you’re hosting on someplace like GitHub and forks are enabled, rewriting history in your fork doesn’t rewrite it in somebody else’s fork, so again, a bad person with a fork from the right time frame could still see your exposed password.
The only fix is to cycle credentials if you ever do accidentally expose a password or key in source control files.
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