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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

Summary

In this chapter, we reviewed two approaches to moving data from the transactional system to the analytics system: logical replication to create a near-real-time solution, and batch copying, which is much simpler to implement but does not support real-time analytics.

Because the batch-copy approach is straightforward to implement and operate, it is often used as a starting point. It can create the same data model as the logical replication approach, but it introduces significant delays between the transactional and analytics systems. Also, in very large data warehouses, a complete refresh of the data requires lengthy batch windows and can quickly become impractical.

Both solutions can be used for the first two steps of the ELT process introduced in this chapter, and they create the data foundation for the transformation step of the ELT process, which will be discussed in the next chapter.

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