作者：David Yard · SQLYARD.com · 2026年4月 · 預估閱讀時間：35–45 分鐘 目錄 簡介 — 什麼是 Microsoft Fabric？為何重要？ 為何選擇 Fabric？商業與技術理由 為何不選 Fabric？誠實的取捨分析 Fabric 所有主要元件說明 OneLake — 一切的基礎 Lakehouse — 資料的家 Data Warehouse — 湖上的 T-SQL 規模化 Pipeline 與 Data Factory — 編排資料流程 Dataflow Gen2 — 低程式碼轉換 Notebook 與 Spark — 程式碼優先工程 Direct Lake — 無需匯入重新整理的 BI 即時智慧 — […]
Microsoft Fabric 完全指南 — 從 OneLake 到金層 | SQLYARD Read More »
By David Yard · SQLYARD.com · April 2026 · Estimated read: 35–45 min Table of Contents Introduction — What Is Microsoft Fabric and Why Should You Care? Why Fabric? The Business and Technical Case Why NOT Fabric? Honest Trade-offs Every Major Fabric Component Explained OneLake — The Foundation of Everything Lakehouse — Where Your Data
SQL Server environments rarely fail suddenly. Most performance problems develop slowly through patterns such as increasing CPU usage, growing I/O pressure, blocking chains, or poorly performing queries. Experienced database administrators understand that the key to stable systems is proactive monitoring, not reactive troubleshooting. Modern monitoring systems collect telemetry continuously and provide early warning signals before
Building a Complete SQL Server Health Monitoring System with AI Read More »
Introduction At this stage, the system is no longer theoretical. What you’ve built across this series is a fully structured AI-driven data layer that replaces: with: But building the logic is only half the problem. The real challenge is turning that logic into a system that: This post completes that transition. The Correct Architecture This
Full Implementation: Metadata-Driven Retrieval, Scoring, and Execution Pipeline Introduction By now, the pattern should be clear. In Part 1, we solved control.In Part 2, we introduced retrieval.In Part 3, we refined similarity and scoring. But none of that matters unless it turns into a system that actually works end to end. This is where most
SQL Server AI Layer — Part 4 Read More »
Similarity Scoring, Retrieval Design, and Performance at Scale The Real Challenge In Part 2, we introduced retrieval. Instead of generating SQL every time, we: Now comes the hard part: How do you actually find the right query to reuse? Because retrieval only works if your matching is accurate. If it’s not: What This Part Solves
SQL Server AI Layer — Part 3 Read More »
Retrieval, Feedback, and Real Improvement The Problem Nobody Solves In Part 1, we built a controlled layer on top of SQL Server. That gave us something most systems never address: A setup that is safe to run in production. But safety alone is not enough. The real question is simple: How does the system improve
SQL Server AI Layer — Part 2 Read More »
Leave a Comment / AI for Data, Articles / By SQLYARD Most SQL Server environments still run on the same foundation they always have. Stored procedures, views, functions, and prebuilt reports. That foundation is solid. But it creates a constant bottleneck. Every new business question requires a developer, new SQL code, a deployment cycle, and
Rethinking SQL Server & PostgreSQL Access, Performance, and Optimization 🔥 Introduction Most database environments still follow the same pattern: Applications query the database directly DBAs build stored procedures and viewsPerformance tuning happens after something slows down It works, but it creates bottlenecks. Every new request turns into: a new stored procedurea new viewanother deploymentmore long-term
🚀 The AI Data Layer Above Relational Databases Read More »
Artificial Intelligence is no longer a side initiative. It is becoming a core capability across modern data platforms. The issue most organizations face is not AI adoption, it is lack of structure. Teams begin using AI tools without standards, governance, or performance awareness. This leads to: Inconsistent query resultsSecurity risksDuplicated effortIncreased cloud cost This is
