MASTERING T-SQL IN SQL SERVER: THE COMPLETE DATABASE GUIDE: QUERY, DEVELOP, AND BUILD AI APPLICATIONS WITH T-SQL. NATIVE VECTOR SEARCH, JSON, REGULAR EXPRESSIONS, AND PRODUCTION DATABASE DEVELOPMENT - Softcover

SULLIVAN, EERO

9798275377361: MASTERING T-SQL IN SQL SERVER: THE COMPLETE DATABASE GUIDE: QUERY, DEVELOP, AND BUILD AI APPLICATIONS WITH T-SQL. NATIVE VECTOR SEARCH, JSON, REGULAR EXPRESSIONS, AND PRODUCTION DATABASE DEVELOPMENT

Softcover

ISBN 13: 9798275377361

Publisher: Independently published, 2025

View all copies of this ISBN edition

2 Used

From � 28.55

3 New

From � 24.90

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.

"synopsis" may belong to another edition of this title.