SQL Server

I’m excited to share a new open-source project I’ve been working on that combines two of my favorite areas: SQL Server and Pure Storage FlashArray performance monitoring. If you’ve been following my blog, you know I am passionate about creating tools that bridge the gap between database platforms and storage infrastructure.

SQL Server performance troubleshooting has always been a unique challenge, especially when it comes to understanding the complete I/O path from the database to storage. Traditionally, database administrators (DBAs) and storage administrators have used separate monitoring tools, which makes it difficult to correlate performance issues across the entire stack. Literally two different perspectives of their performance worlds.

SQL Server 2025 introduces a new compression algorithm called ZSTD (Zstandard), which can enhance database backup performance. Using ZSTD allows for greater control over backup performance, particularly concerning CPU usage and backup duration. I recently conducted some preliminary benchmarks comparing ZSTD at its three compression levels with the existing MS_XPRESS algorithm. The results are compelling and provide additional options for managing database backup performance effectively.

The Test Setup

I set up two types of tests on a 389.37GB database to highlight the performance characteristics of the backup compression algorithms:

In this post, I’ll walk you through a T-SQL script that creates application-consistent snapshots on Pure Storage FlashArray, all from within SQL Server, no external tooling. SQL Server 2025 introduces a powerful new feature: the sp_invoke_external_rest_endpoint stored procedure. This enhancement makes calling REST APIs directly from T-SQL easier than ever. Combining this new capability with Pure Storage’s API allows us to orchestrate snapshot operations seamlessly, with no external tools or scripts required.

With the release of SQL Server 2025, Microsoft is firmly positioning its flagship database platform as an enterprise-ready AI solution. Having spent time with the preview builds and working with the feature set, I believe this release represents a significant opportunity for organizations to leverage AI capabilities and their existing investments in SQL Server infrastructure, skills, and processes.

While many cloud-native and niche vector databases have emerged to support AI workloads, SQL Server 2025 brings these capabilities to your existing operational data platform. This integration offers unique advantages that standalone AI solutions can’t match without significant integration efforts and security considerations.

As AI continues to evolve, many of us are looking for ways to leverage large language models (LLMs) without relying on cloud services. As we learned in my previous post “Using a Local Large Language Model (LLM): Running Ollama on Your Laptop”, running models locally gives you complete control over your data, eliminates API costs, and can be integrated seamlessly into your existing workflows. Today, I’d like to share how you can interact with local LLMs using PowerShell through the Ollama API. I’m choosing PowerShell for this implementation to showcase the accessibility and simplicity of building a chatbot through the command line, allowing you to get started with this experience quickly and smoothly.

You can now run powerful LLMs like Llama 3.1 directly on your laptop using Ollama. There is no cloud, and there is no cost. Just install, pull a model, and start chatting, all in a local shell.

Large Language Models (LLMs) have revolutionized how we interact with data and systems, but many assume you need significant cloud resources or specialized hardware to run them. Today, I want to walk you through getting started with Ollama, an approachable tool that lets you run large language models locally on your laptop.

In this post, the fifth in our series, I want to illustrate an example of using the T-SQL Snapshot Backup feature in SQL Server 2022 to seed Availability Groups (AGs) with storage-based snapshots. Efficiently seeding an Availability Group is essential for maintaining high availability and ensuring effective disaster recovery. With the introduction of T-SQL Snapshot Backup in SQL Server 2022, snapshots can now be created at the storage layer. This advancement significantly speeds up the initialization of secondary replicas, particularly in environments that handle large databases.

In this post, the fourth in our series, I want to share an example demonstrating SQL Server 2022’s T-SQL Snapshot Backup feature in a scenario where a database spans multiple storage arrays. If you’re dealing with multi-array environments, you’ll appreciate how this technique freezes database write I/O to take coordinated snapshots across volumes on two FlashArrays. In this post, I’ll walk you through the process, point out some of the script’s key elements, and show you how long the write I/O pause takes.

SQL Server 2025 is an upcoming release focused on AI, analytics, and modern database development, backed by innovations in mission-critical engine features for security, performance, and high availability.

Let’s dive into what this means for your AI and data-driven applications. We’ll walk through some of the new features announced by Azure Data Principal Architect Bob Ward in his post Announcing SQL Server 2025, and I’ll give some thoughts on these innovations and where they will fit in the enterprise.

In this post, the third in our series on using T-SQL Snapshot Backup, I will guide you through using the new T-SQL Snapshot Backup feature in SQL Server 2022 to take a snapshot backup and then perform point-in-time database restores using that snapshot backup as the base, but this time using an Azure Virtual Machine. We will explore how to manage Azure storage-level operations, such as taking snapshots, cloning snapshots, and executing an instantaneous point-in-time database restore from the snapshot with minimal impact on your infrastructure. Additionally, I will demonstrate a PowerShell script that utilizes dbatools and Azure Az modules to automate the process.