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  • Book Overview & Buying AI-Ready PostgreSQL 18
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AI-Ready PostgreSQL 18

AI-Ready PostgreSQL 18

By : Vibhor Kumar, Marc Linster
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AI-Ready PostgreSQL 18

AI-Ready PostgreSQL 18

By: Vibhor Kumar, Marc Linster
Buy this Book

Overview of this book

In today’s data-first world, businesses need applications that blend transactions, analytics, and AI to power real-time insights at scale. Mastering PostgreSQL 18 for AI-Powered Enterprise Apps is your essential guide to building intelligent, high-performance systems with the latest features of PostgreSQL 18. Through hands-on examples and expert guidance, you’ll learn to design architectures that unite OLTP and OLAP, embed AI directly into apps, and optimize for speed, scalability, and reliability. Discover how to apply cutting-edge PostgreSQL tools for real-time decisions, predictive analytics, and automation. Go beyond basics with advanced strategies trusted by industry leaders. Whether you’re building data-rich applications, internal analytics platforms, or AI-driven services, this book equips you with the patterns and insights to deliver enterprise-grade innovation. Ideal for developers, architects, and tech leads driving digital transformation, this book empowers you to lead the future of intelligent applications. Harness the power of PostgreSQL 18—and unlock the full potential of your data.
Table of Contents (28 chapters)
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Preface
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Preface
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1
Part 1: Introducing PostgreSQL and Setting the Stage
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Part 1: Introducing PostgreSQL and Setting the Stage
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Chapter 1: Transactional, Analytical, and AI Business Systems and Their Database Requirements
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Chapter 1: Transactional, Analytical, and AI Business Systems and Their Database Requirements
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Why do we have multiple types of databases?
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How are transactional, analytical, and AI application requirements different?
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Historical reasons for specialized solutions
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Multiple specialty solutions are an architectural challenge
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Summary
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Chapter 2: The Evolution of Postgres
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Chapter 2: The Evolution of Postgres
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The evolution of key capabilities for transactional, analytical, and AI use cases
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What makes PostgreSQL the (almost) universal database solution today
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When to complement PostgreSQL
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Summary
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Chapter 3: A Sample E-Commerce Application
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Chapter 3: A Sample E-Commerce Application
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Technical requirements
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The sample application
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The microservices and their data models
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Users and groups
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Coding conventions
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Installing the e-commerce sample application
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Summary
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Part 2: Creating Transactional Applications
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Part 2: Creating Transactional Applications
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Chapter 4: Database Requirements for High-Volume Transactional Applications
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Chapter 4: Database Requirements for High-Volume Transactional Applications
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Technical requirements
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Why microservices need a smarter database architecture
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Using PostgreSQL to create successful microservices data architectures
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PostgreSQL constructs to bring it all together
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Security
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Summary
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Chapter 5: Transactions, ACID Compliance, and Data Normalization
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Chapter 5: Transactions, ACID Compliance, and Data Normalization
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Technical requirements
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ACID compliance: What is it and why does it matter?
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Working with transactions in PostgreSQL
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A closer look at isolation levels and their importance
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Controlling isolation levels and database locks
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Data modeling for high performance
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Summary
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Chapter 6: Functions and Stored Procedures
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Chapter 6: Functions and Stored Procedures
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Technical requirements
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Functions or Procedures? What's the difference?
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Procedural languages in Postgres
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Summary
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Chapter 7: Designing for High Transaction Volumes and Writing Efficient Transactional Code
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Chapter 7: Designing for High Transaction Volumes and Writing Efficient Transactional Code
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Technical requirements
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PostgreSQL data types
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PostgreSQL indexes
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Guidelines for writing effective, efficient, and manageable queries
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Summary
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Chapter 8: Understanding and Monitoring Transactional Application Performance
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Chapter 8: Understanding and Monitoring Transactional Application Performance
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Technical requirements
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Understanding PostgreSQL's state and its transactional performance
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Other useful Postgres extensions
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Actionable takeaways for developers
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Summary
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Part 3: Creating Analytical Applications
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Part 3: Creating Analytical Applications
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Chapter 9: Database Requirements for Analytical Use Cases
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Chapter 9: Database Requirements for Analytical Use Cases
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Business requirements for analytics solutions
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Technical implications and how to meet them in PostgreSQL
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Leveraging PostgreSQL's ecosystem
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Summary
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Chapter 10: The Analytics Data Model
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Chapter 10: The Analytics Data Model
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Star schemas
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Data vaults
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Relational online analytics processing (ROLAP)
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Comparing ROLAP, star schemas, and data vaults
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Summary
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Chapter 11: Feeding the PostgreSQL Data Warehouse
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Chapter 11: Feeding the PostgreSQL Data Warehouse
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Technical requirements
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How to use logical replication to feed the data warehouse
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How to use the COPY command to feed the data warehouse
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Summary
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Chapter 12: Transforming the Data for Analytics
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Chapter 12: Transforming the Data for Analytics
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Technical requirements
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Three ways to create an analytics-focused star schema
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Leveraging partitioning
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Guidance for the developer
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Summary
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Chapter 13: PostgreSQL's Analytics Functions
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Chapter 13: PostgreSQL's Analytics Functions
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Technical requirements
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PostgreSQL's analytics functions
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Groups and aggregates
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Window functions
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CTEs
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Summary
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Chapter 14: Analyzing Text Documents in PostgreSQL
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Chapter 14: Analyzing Text Documents in PostgreSQL
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Technical requirements
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Text as a key data type in advanced analytics
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Text search in PostgreSQL
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Guidance for developers
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Summary
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Part 4: Using PostgreSQL as an AI Platform
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Part 4: Using PostgreSQL as an AI Platform
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Chapter 15: Database Requirements for Artificial Intelligence Use Cases
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Chapter 15: Database Requirements for Artificial Intelligence Use Cases
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Working with natural language and unstructured data
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Why is nearest neighbor search crucial for AI?
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Summary
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Chapter 16: Vectors and Indexing for AI with pgvector
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Chapter 16: Vectors and Indexing for AI with pgvector
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What is a vector?
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Real AI vectors
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What is pgvector?
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Indexing for AI: Finding the needle in the haystack
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Semantic search with pgvector
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Using pgvector to build a recommendation engine
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Summary
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Chapter 17: Multi-Modal Reasoning: Combining AI Vectors with Standard SQL
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Chapter 17: Multi-Modal Reasoning: Combining AI Vectors with Standard SQL
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The power of multi-modal reasoning
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Why vectors and SQL belong in the same platform
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Designing high-performance hybrid queries
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Real-world examples and patterns
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Architecture: Why this matters
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Summary
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Chapter 18: Step-by-Step Approach to Integrating LLMs with PostgreSQL to Create Complete AI Applications
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Chapter 18: Step-by-Step Approach to Integrating LLMs with PostgreSQL to Create Complete AI Applications
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Technical requirements
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Creating an embedding with cURL
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Creating embeddings in PostgreSQL with PL/PythonU
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Summary
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Chapter 19: Production-Ready AI Embedding Pipeline Patterns
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Chapter 19: Production-Ready AI Embedding Pipeline Patterns
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Technical requirements
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Why do we need a pipeline?
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What are the requirements of an AI pipeline?
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Building an asynchronous, high-performance, and reliable pipeline
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Making the async pipeline near-real-time
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How do I choose the right pipeline model?
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On-demand versus cached query embeddings, and what happens when models change
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A hard truth: Model changes create semantic drift
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Summary
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Chapter 20: PostgreSQL and MCP: The Blueprint for a Robust AI Assistant
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Chapter 20: PostgreSQL and MCP: The Blueprint for a Robust AI Assistant
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What is the MCP? A simple explanation
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Building an AI chatbot in PostgreSQL
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The dynamic SQL chatbot
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Introducing the MCP: The contract between models and tools
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The MCP and the core idea: One system, two truths
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Operational checklists and the extension path
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Summary
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Chapter 21: Tying It All Together
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Chapter 21: Tying It All Together
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PostgreSQL capabilities
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Key PostgreSQL 18 innovations
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PostgreSQL and the AI revolution
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PostgreSQL: A data platform for the future
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Chapter 22: Unlock Access to the Code Bundle and the PDF Version
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Chapter 22: Unlock Access to the Code Bundle and the PDF Version
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Unlock this book's free benefits in three easy steps
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Index
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Index

Summary

In this chapter, we went through the third and final step of the Extract, Load, and Transform (ELT) process. PostgreSQL provides multiple ways to transform data from a normalized, transaction-optimized model into a denormalized, analytics-optimized model: views, materialized views, tables, and triggers. While views are easy to implement, they don't scale to larger data volumes or user counts. Based on a performance analysis, we recommend two options: materialized views or tables and triggers. They are the way to go when creating a real-world analytics-focused data model. The choice is driven by implementation complexity and the need for near-real-time insights.

The next chapter introduces PostgreSQL's native analytics query capabilities that build on the analytics data model: GROUPS, GROUPING SETS, ROLLUPS, CUBES, WINDOW FUNCTIONS, and COMMON TABLE EXPRESSIONS (CTE). Those are the SQL constructs that we use to create reports and analyze data.

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