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Vector Database Development Systems: Designing, Building, and Scaling High Performance Semantic Search, Retrieval, and Driven Data Platforms
Paperback
Publisher: Independently Published
Published: Jan 5 2026
Pages: 128
Weight: 0.52
Height: 0.27 Width: 7.00 Depth: 10.00
Language: English
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 data
- How vector databases differ from relational and document databases
- Indexing techniques for fast similarity search (approximate and exact)
- Designing ingestion pipelines for embeddings and unstructured data
- Query optimization and relevance tuning for semantic search
- Building scalable architectures for large-scale AI workloads
- Integrating vector databases into AI applications and workflows
- Operational concerns: performance, storage, updates, and monitoring
- Security, data governance, and system reliability considerations
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.
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