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Author: Kevin Feasel

Architectural Guidance for IoT Deployments in Azure

Bhimraj Ghadge shares some tips:

Edge computing, a strategy for computing on location where data is collected or used, allows IoT data to be gathered and processed at the edge, rather than sending the data back to a data center or cloud. Together, IoT and edge computing are a powerful way to rapidly analyze data in real-time.

In this Tutorial, I am trying to lay out the components and considerations for designing IoT solutions based on Azure IoT and services.

Read on for an overview of IoT components in Azure, as well as several things to keep in mind during systems design and implementation.

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Resetting the sa Password on a Locked-Out SQL Server Instance

Tim Radney jimmies the lock:

Getting locked out of a SQL Server instance can happen due to a number of situations. The most common scenario I’ve encountered is when a SQL Server is moved from one domain to another without the domain being trusted or having a local SQL admin account. I recently encountered another incident where a DBA was attempting to fail over a log-shipped instance to another instance. Part of their run book included a script to disable a set of users to block production access to the instance. The problem was, the production instance was on a cluster, unlike the development and QA systems where the script had been tested. Disabling the users in production took down the instance preventing the tail log backups from occurring. We had to get the instance back up in order to take those final backups.

What can you do if you find that you’re locked out of a SQL Server instance?

Read on for that answer.

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August 2025 Microsoft Fabric Feature Summary

Patrick LeBlanc has a list:

The August 2025 Fabric Feature Summary showcases several exciting updates designed to streamline workflows and enhance platform capabilities. Notably, users will benefit from the new flat list view in Deployment pipelines, making navigation and management more intuitive. In addition, expanded support for service principals and cross-tenant integration with Azure DevOps reflects Microsoft’s commitment to versatile and secure enterprise solutions.

As usual, there’s a lot on the list to digest, but Patrick does a great job of laying out the content in an accessible manner.

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The Basics of Log Shipping

Kevin Hill explains why log shipping is still a viable disaster recovery approach, 25 years later:

In a world where shiny new HA/DR features get all the press, there’s one SQL Server technology that just keeps doing its job.

Log Shipping has been around since SQL Server 2000. It doesn’t make headlines, it doesn’t have fancy dashboards, and it’s not going to win you any architecture awards. But for the right environment and use case, it’s rock solid and can save your bacon job in a disaster.

Read on for a briefing on the topic.

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Replacing Text in SQL Server 2025 via Regular Expression

Louis Davidson continues a series on regular expressions in SQL Server 2025:

Okay, we have gone through as much of the RegEx filtering as I think is a a part of the SQL Server 2025 implementation. Now it is time to focus on the functions that are not REGEXP_LIKE. We have already talked about REGEXP_MATCHES, which will come in handy for the rest of the series.

I will start with REGEXP_REPLACE, which is like the typical SQL REPLACE function. But instead of replacing based on a static delimiter, it can be used to replace multiple (or a specific) value that matches the RegEx expression. All of my examples for this entry will simply use a variable with a value we are working on, so no need to create or load any objects.

Read on to see how it works, including plenty of examples.

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Time Series Helpers in NumPy

Bala Priya C shares some one-liners:

NumPy’s array operations can help simplify most common time series operations. Instead of thinking step-by-step through data transformations, you can apply vectorized operations that process entire datasets at once.

This article covers 10 NumPy one-liners that can be used for time series analysis tasks you’ll come across often. Let’s get started!

Click through to see the ten in action.

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Migrating Azure Data Studio SQL Notebooks to VS Code Polyglot Notebooks

Haroon Ashraf gives us a somewhat unwieldy process:

As a SQL/BI developer, I want to run and store my SQL scripts and documentation efficiently in a Notebook as an alternative to using Azure Data Studio SQL Notebooks since Azure Data Studio is retiring soon. Read on to learn more about Visual Studio Code Polyglot Notebooks.

I liked the simplicity of having a SQL kernel in Azure Data Studio. Haroon shows how to work around it and get to roughly the same spot, but I do hope the SQL Server tools team is able to migrate that SQL kernel over to VS Code prior to Azure Data Studio’s ultimate demise.

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Using VALUES in Iterators in DAX

Marco Russo and Alberto Ferrari answer a question:

In a previous article, Choosing between DISTINCT and VALUES in DAX, we explained how to choose the proper function to iterate the unique values visible in a column in the current filter context. We suggest reading that article before starting this one, because it describes the same underlying problem. Here, we discuss whether to use VALUES in an iterator. This choice depends on the answer to this question: Do you want to include or exclude the blank row generated by an invalid relationship when iterating over the rows of a table reference?

Read on to see how the answer affects your decision.

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Materializing Lake Views in Microsoft Fabric

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.

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A Primer on ACID Compliance

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.

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