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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.

Update (February 2026): This post has been updated to reflect the current best practices for monitoring FlashArray using OpenMetrics. Starting with Purity//FA 6.7.x, FlashArray includes a native OpenMetrics exporter, eliminating the need for a separate exporter service/container. The configuration examples below now demonstrate direct-to-array scraping.

This post introduces you to monitoring your Pure Storage FlashArray using OpenMetrics. It shows you how to get started quickly using Docker Compose to monitor your Pure Storage FlashArray environment.

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.

In this post, the second in our series, I will guide you through using the new T-SQL Snapshot Backup feature in SQL Server 2022 to take a snapshot backup and perform point-in-time database restores using a snapshot backup as the base of the restore. We will explore how to manage storage-level operations, such as cloning snapshots and executing an instantaneous point-in-time restore of a database from the snapshot with minimal impact on your infrastructure. Additionally, I will demonstrate a PowerShell script that utilizes dbatools and the PureStoragePowerShellSDK2 modules to automate the process.

Traditional SQL Server backups can struggle with large databases, resulting in longer backup times and resource contention. T-SQL Snapshot Backup, a new feature in SQL Server 2022, addresses these challenges by allowing storage-based snapshots to be coordinated through T-SQL. This feature delivers faster, more efficient backups, especially for large-scale environments with the most aggressive of recovery objectives.

Anatomy of a Full Backup

Before we start learning about T-SQL Snapshot backup, let’s establish what a backup in SQL Server is. When a backup is initiated in SQL Server, several things occur. Follow along in Figure 1 below; here, you see a database with database files on Disk A and a transaction log file on Disk B.

Welcome back to the fifth installment of our blog series on using the Pure Storage PowerShell SDK2. In this post, we’re diving into a hands-on demonstration of using the Pure Storage FlashArray API to track replication performance between two arrays. This is especially useful for DBAs and storage admins, who must ensure their data replication processes run smoothly and efficiently. A typical scenario here is ensuring a snapshot is entirely replicated between sites before kicking off some other process. Following this demo, you’ll learn how to monitor replication status and verify the completion of snapshot transfers between arrays.