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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

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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1
Part 1: Introducing PostgreSQL and Setting the Stage
5
Part 2: Creating Transactional Applications
11
Part 3: Creating Analytical Applications
18
Part 4: Using PostgreSQL as an AI Platform
27
Index

9

Database Requirements for Analytical Use Cases

Modern businesses operate in an environment where data-driven decision-making is essential, not optional. Historically, databases were built to process and record transactions—capturing the operational heartbeat of an organization. However, as competition intensifies and market conditions shift more rapidly, relying solely on transactional systems without integrated analytical capabilities puts companies at a disadvantage because their business decisions are not data-driven. This makes them slow to react to changing market conditions. Agile companies must analyze their operational data in real time to remain nimble and competitive.

Decision cycles that once took weeks or months now shrink to days or even hours. This shift means analytics can no longer be a separate step done after the fact; instead, it must be integrated into the same systems that handle daily transactions. By combining analytics directly with transactional...

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AI-Ready PostgreSQL 18
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