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Mastering T-SQL in SQL Server: The Complete Database Guide: Query, Develop, and Build AI Applications with T-Sql. Native Vector Search, Json, Regular
Paperback
Publisher: Independently Published
Published: Nov 20 2025
Pages: 248
Weight: 0.96
Height: 0.52 Width: 7.00 Depth: 10.00
Language: English
Build faster, safer, and more capable SQL Server systems by mastering modern T-SQL from core querying through AI-ready vector search.
Many developers can write queries that work, but struggle when data grows, workloads mix OLTP and reporting, and new requirements like JSON, regex text processing, and semantic search arrive. The result is fragile code, slow performance, and features that are hard to ship confidently.
This guide helps you move past that ceiling. You will learn how SQL Server executes T-SQL, how to model real systems, and how to write production queries that stay correct and predictable under pressure, including native vector search workflows for AI applications.
- select filter sort and paginate results with clear intent and stable performance
- write reliable joins subqueries and apply patterns for real retrieval problems
- use grouping aggregation and window functions for analytics and reporting
- choose data types that protect correctness and improve execution plans
- build expressions and conditional logic that stay readable and sargable
- work confidently with dates times and time series functions
- design stored procedures and user defined functions for reusable logic
- control transactions isolation levels and concurrency in multi user systems
- handle errors with try catch throw and xact safe patterns
- read execution plans design indexes and use query store to spot regressions
- apply intelligent query processing and handle parameter sensitive plans
- model schemas that survive growth including partitioning and archival strategies
- clean parse and validate text with string functions and native regex support
- store vectors run exact and approximate similarity search and index embeddings
- design embedding tables that connect semantic results to business data
- prepare chunked data implement semantic search and build rag pipelines with external models
- secure databases with authentication authorization row level security and auditing
- protect data with encryption backup restore and high availability planning
- manage the database lifecycle with dacpacs testing and ci cd for sql server and azure sql
- avoid common query and schema anti patterns and ship with a production checklist
Throughout the book you will find working T-SQL patterns and scripts that you can adapt directly into your own projects, from day to day querying to vector based semantic search.
Grab your copy today and start writing T-SQL that holds up in production and supports modern AI features.
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