課程目標:
本課程旨在提供學員設計、實作、優化和管理現代資料庫應用所需的核心技術與實戰能力。從資料正規化、資料庫管理 (DBA) 到雲端、NoSQL 和新興向量資料庫，全面掌握多樣化的資料儲存解決方案。
課程單元與內容細項
單元一.關聯式資料庫設計與正規化:探討資料庫建模的理論基礎，並實作表格的正規化，確保資料的一致性與效率。
單元二.資料庫管理與操作實務 (DBA):以 MS SQL Server 為主要操作平台，同時介紹 SQLite、Access、MySQL 等常用資料庫系統的特性與應用場景。
2.1 索引優化策略:索引的設計、建立與維護，以提升查詢效率。
2.2 資料庫空間管理:磁碟空間的規劃、檔案群組與成長性管理。
2.3 安全與權限管理:使用者帳號、角色 (Role) 建立與權限授予，確保資料安全性。
2.4 效能監控與調校: 監控工具的使用，識別性能瓶頸並進行優化。
單元三.結構化查詢語言 (SQL) 與工具整合:深入學習標準 SQL 指令（DML, DDL, DCL）的操作。透過比較 Access、Excel VBA 等工具，探討不同應用場景下的 SQL 實作差異。
3.1 SQL 指令效能調校執行計畫 (Execution Plan) 分析與查詢優化技巧。
單元四.資料庫安全與前端應用:探討資料庫應用層面的安全問題與前端工具的整合。
4.1 SQL Injection: 防護介紹攻擊原理與預防機制 (如參數化查詢)。
4.2 資料庫連接與類別設計:介紹常見的資料庫連接技術 (如 ODBC, JDBC) 與實用的資料庫操作類別 (Class) 設計。
單元五.雲端與大數據資料庫基礎:介紹雲端資料庫服務模型，並探討大數據儲存與處理技術。
5.1 雲端資料庫概論:探討 Hadoop 的運作原理，並以 MS Azure HDInsight 為例進行介紹或基礎實作。
單元六.非關聯式 (NoSQL) 資料庫實務:深入探討 NoSQL 的種類與適用情境，實作開放性資料 (Open Data) 的截取、儲存與查詢。
單元七.AI新興趨勢：向量資料庫 (Vector DB)探討向量資料庫的功能、結構及其在人工智慧 (AI) 應用中的關鍵作用 (如 RAG 架構中的應用)。
課程特色:
一.理論與實務並重： 課程結合正規化理論與主流資料庫系統的操作實務。
二.技術廣度與深度： 涵蓋傳統 DBA 技能、Web 安全、雲端架構及最新的 AI 資料庫趨勢。
《 課程簡介 -- English 》
Database Management Design and Practice Course Outline
▓Course Objective:
This course aims to equip students with the core technologies and practical skills necessary for designing, implementing, optimizing, and managing modern database applications. Students will gain a comprehensive mastery of diverse data storage solutions, ranging from data normalization and Database Administration (DBA) to Cloud, NoSQL, and emerging Vector Databases.
▓Course Modules and Detailed Content:
Unit I: Relational Database Design and Normalization
Exploration of database modeling theoretical foundations and practical implementation of table normalization to ensure data consistency and efficiency.
Unit II: Database Administration (DBA) and Operations Practice
Focusing on MS SQL Server as the primary platform, while also introducing the characteristics and application scenarios of commonly used database systems such as SQLite, Access, and MySQL.
2.1 Index Optimization Strategy: Design, creation, and maintenance of indexes to enhance query efficiency.
2.2 Database Space Management: Planning of disk space, file groups, and growth management.
2.3 Security and Permission Management: Creation of user accounts and roles, and granting permissions to ensure data security.
2.4 Performance Monitoring and Tuning: Use of monitoring tools to identify performance bottlenecks and conduct optimization.
Unit III: Structured Query Language (SQL) and Tool Integration
In-depth study of standard SQL commands (DML, DDL, DCL) operations. Comparison of SQL implementation differences across various application scenarios through tools like Access and Excel VBA.
SQL Command Performance Tuning: Analysis of Execution Plans and query optimization techniques.
Unit IV: Database Security and Front-end Applications
Exploration of security issues at the database application layer and integration with front-end tools.
4.1 SQL Injection Protection: Introduction to attack principles and prevention mechanisms (e.g., parameterized queries).
4.2 Database Connectivity and Class Design: Introduction to common database connection technologies (e.g., ODBC, JDBC) and practical design of database operation classes.
Unit V: Fundamentals of Cloud and Big Data Databases
Introduction to Cloud Database Service Models and exploration of big data storage and processing technologies.
5.1 Cloud Database Overview: Exploration of the working principles of Hadoop, with an introduction or basic practice using MS Azure HDInsight as an example.
Unit VI: Non-Relational (NoSQL) Database Practice
In-depth exploration of NoSQL types and applicable scenarios, with practical implementation of capturing, storing, and querying Open Data.
Unit VII: Emerging AI Trends: Vector Databases (Vector DB)
Exploration of the functionality, structure, and critical role of Vector Databases in Artificial Intelligence (AI) applications (e.g., applications within the RAG architecture).
▓Course Features
I. Theory and Practice Integration: The course combines normalization theory with the operational practices of mainstream database systems.
II. Technical Breadth and Depth: Covers traditional DBA skills, Web security, Cloud architecture, and the latest AI database trends.
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