Vector Database Development Systems (Paperback)

Yuan Zhu

ISBN 13: 9798242733299

Published by Independently Published, 2026

About this Item

Paperback. Vector databases have become a foundational component of modern AI systems powering semantic search, recommendation engines, retrieval-augmented generation (RAG), and intelligent data applications. As traditional databases struggle with high-dimensional data, vector-based systems offer a new paradigm for storing, indexing, and querying meaning at scale.Vector Database Development Systems is a comprehensive, hands-on guide for developers, architects, and AI practitioners who want to design, build, and operate robust vector database solutions from the ground up.This book goes beyond theory to explain how vector databases actually work internally, including embeddings, similarity metrics, indexing strategies, memory layouts, and query execution pipelines. You'll learn how to architect vector-first systems that are fast, scalable, and production-ready whether running locally, on-premise, or in distributed cloud environments.What You'll Learn: Core concepts behind vector representations and high-dimensional dataHow vector databases differ from relational and document databasesIndexing techniques for fast similarity search (approximate and exact)Designing ingestion pipelines for embeddings and unstructured dataQuery optimization and relevance tuning for semantic searchBuilding scalable architectures for large-scale AI workloadsIntegrating vector databases into AI applications and workflowsOperational concerns: performance, storage, updates, and monitoringSecurity, data governance, and system reliability considerationsRather than focusing on a single tool or vendor, this book emphasizes system-level thinking giving you transferable knowledge that applies across open-source, self-hosted, and managed vector database platforms.Whether you are building AI-powered search, recommendation systems, intelligent assistants, or next-generation data platforms, this book equips you with the technical depth and practical insight needed to succeed.Written in clear, structured language with real-world design patterns, Vector Database Development Systems serves as both a learning guide and a long-term reference for professionals working at the intersection of databases, machine learning, and modern software architecture. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9798242733299

Bibliographic Details

• Title: Vector Database Development Systems (Paperback)
• Publisher: Independently Published
• Publication Date: 2026
• Language: English
• ISBN 13: 9798242733299
• Binding: Paperback
• Condition: new

About this title

Synopsis

Vector databases have become a foundational component of modern AI systems powering semantic search, recommendation engines, retrieval-augmented generation (RAG), and intelligent data applications. As traditional databases struggle with high-dimensional data, vector-based systems offer a new paradigm for storing, indexing, and querying meaning at scale.
Vector Database Development Systems is a comprehensive, hands-on guide for developers, architects, and AI practitioners who want to design, build, and operate robust vector database solutions from the ground up.
This book goes beyond theory to explain how vector databases actually work internally, including embeddings, similarity metrics, indexing strategies, memory layouts, and query execution pipelines. You’ll learn how to architect vector-first systems that are fast, scalable, and production-ready whether running locally, on-premise, or in distributed cloud environments.
What You’ll Learn:

1. Core concepts behind vector representations and high-dimensional data
2. How vector databases differ from relational and document databases
3. Indexing techniques for fast similarity search (approximate and exact)
4. Designing ingestion pipelines for embeddings and unstructured data
5. Query optimization and relevance tuning for semantic search
6. Building scalable architectures for large-scale AI workloads
7. Integrating vector databases into AI applications and workflows
8. Operational concerns: performance, storage, updates, and monitoring
9. Security, data governance, and system reliability considerations

Rather than focusing on a single tool or vendor, this book emphasizes system-level thinking giving you transferable knowledge that applies across open-source, self-hosted, and managed vector database platforms.
Whether you are building AI-powered search, recommendation systems, intelligent assistants, or next-generation data platforms, this book equips you with the technical depth and practical insight needed to succeed.
Written in clear, structured language with real-world design patterns, Vector Database Development Systems serves as both a learning guide and a long-term reference for professionals working at the intersection of databases, machine learning, and modern software architecture.