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PostgreSQL with AI: A Complete Beginner's Guide to PostgreSQL Databases, Query Optimization, Analytics, and AI-Powered Data Systems
Paperback
Publisher: Independently Published
Published: Jun 7 2026
Pages: 166
Weight: 0.51
Height: 0.35 Width: 6.00 Depth: 9.00
Language: English
Master the world's most advanced open-source relational database - the one powering Apple, Instagram, Spotify, and Supabase - and build AI-powered data systems on top of it, from scratch.
PostgreSQL with AI takes you from your very first SQL query through advanced query optimization, window functions, JSONB, full-text search, replication, cloud deployment, and pgvector - and then into integrating modern AI APIs to build semantic search engines, intelligent recommendation systems, and AI-driven data pipelines. Written for the 2026 ecosystem, every chapter is practical, current, and built around real-world database engineering.
PostgreSQL is not just another SQL database. Its JSONB document storage, declarative partitioning, logical replication, advanced full-text search, and the pgvector extension - which turns PostgreSQL into a native vector database for AI embeddings - make it the single most capable open-source database available. In 2026, every major cloud platform runs it: AWS RDS, Google Cloud SQL, Azure Database, Supabase, and Neon. This book teaches you not just how to use it, but how to architect, optimize, and make it intelligent.
What you will learn:
- Install PostgreSQL and write powerful SQL - filtering, sorting, joining, aggregating, and using CTEs
- Design normalized, scalable relational schemas using best-practice data modeling techniques
- Master advanced SQL - window functions, JSONB, full-text search, and stored procedures
- Optimize query performance with indexes, EXPLAIN ANALYZE, and PostgreSQL-specific tuning
- Implement replication, partitioning, backup, and high-availability production deployments
- Deploy and manage PostgreSQL on AWS RDS, Supabase, Google Cloud SQL, and Neon
- Use pgvector for native vector embeddings and semantic search inside PostgreSQL
- Integrate OpenAI and LLM APIs directly into PostgreSQL-backed applications
- Build RAG (Retrieval-Augmented Generation) workflows using pgvector and AI APIs
- Complete five real-world projects: a sales analytics database, AI reporting dashboard, semantic search engine, smart recommendation system, and an AI-assisted monitoring platform
Four parts, no padding:
Part I builds your PostgreSQL foundation - tables, data types, SQL queries, joins, constraints, indexes, transactions, normalization, and performance basics. Part II covers advanced topics including query optimization, window functions, JSONB, full-text search, security, backup, replication, cloud environments, partitioning, and scaling strategies. Part III connects PostgreSQL to the AI era with practical chapters on pgvector, LLM API integration, AI-assisted query generation, prompt engineering, semantic search, RAG pipelines, recommendation systems, and intelligent analytics. Part IV delivers five complete guided projects you build from start to finish.
The book includes a PostgreSQL quick reference, a SQL vs NoSQL decision guide, an index strategy reference, a configuration reference, a common errors and fixes guide, a useful extensions reference, and a recommended learning path - seven appendices covering everything you need.
If you want a database that handles relational data, JSON documents, full-text search, time-series data, geographic queries, and AI vector embeddings - all in one system - PostgreSQL is the answer, and this book is the complete guide.
Perfect for: developers learning their first serious database, backend engineers migrating from MySQL, data engineers building analytics systems, and developers who want to add native AI vector search and RAG capabilities to their database layer using pgvector.
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