PostgreSQL is no longer just a database; it is the backbone of modern, intelligent applications. This book shows you how to take PostgreSQL to the next level by combining advanced database techniques with AI-driven automation and multi-cloud strategies.You will learn how to deploy PostgreSQL across AWS, Azure, and Google Cloud, implement serverless and Kubernetes-based architectures, and design for high availability, disaster recovery, and multi-region resilience. Along the way, you will explore how AI can support query optimization, anomaly detection, observability, and real-time analytics. The book also covers pgvector for semantic search, Retrieval-Augmented Generation, or RAG, for intelligent data retrieval, and in-database machine learning using PGML.Security and compliance are addressed through practical coverage of Zero Trust models, IAM federation, and AI-enhanced threat detection. Hands-on labs, AMIs, and templates help you apply these techniques in real-world environments, from cloud-native deployments to enterprise-scale PostgreSQL operations.By the end of the book, you will have the skills to design, secure, automate, and optimize intelligent PostgreSQL systems for the AI era.What You Will LearnDesign multi-cloud PostgreSQL architectures across AWS, Azure, and GCPImplement AI-driven workflows for query optimization and anomaly detectionUse pgvector and RAG for semantic search and GenAI-powered data retrievalDeploy in-database machine learning with PGMLSecure PostgreSQL with Zero Trust, IAM federation, and AI-enhanced threat detectionAgentic AI development and deployment for PostgreSQL managementManage PostgreSQL on Kubernetes using cloud-native operators
PostgreSQL is no longer just a database; it is the backbone of modern, intelligent applications. This book shows you how to take PostgreSQL to the next level by combining advanced database techniques with AI-driven automation and multi-cloud strategies.
You will learn how to deploy PostgreSQL across AWS, Azure, and Google Cloud, implement serverless and Kubernetes-based architectures, and design for high availability, disaster recovery, and multi-region resilience. Along the way, you will explore how AI can support query optimization, anomaly detection, observability, and real-time analytics. The book also covers pgvector for semantic search, Retrieval-Augmented Generation, or RAG, for intelligent data retrieval, and in-database machine learning using PGML.
Security and compliance are addressed through practical coverage of Zero Trust models, IAM federation, and AI-enhanced threat detection. Hands-on labs, AMIs, and templates help you apply these techniques in real-world environments, from cloud-native deployments to enterprise-scale PostgreSQL operations.
By the end of the book, you will have the skills to design, secure, automate, and optimize intelligent PostgreSQL systems for the AI era.
What You Will Learn
Who this Book Is For
Senior database administrators, system architects, and cloud engineers who want to master advanced PostgreSQL deployments in multi-cloud environments. It’s ideal for professionals looking to integrate AI-driven workflows, optimize performance, and build secure, scalable database systems for enterprise and real-time applications.
Venkateswara Vadlamani
PostgreSQL advanced PostgreSQL cloud architecture PostgreSQL AWS Azure GCP Aurora Serverless PostgreSQL Neon serverless PostgreSQL pgvector vector search Retrieval-Augmented Generation RAG PostgreSQL PostgreSQL GenAI integration PostgreSQL machine learning MADlib PL/Python PostgreSQL AI PostgreSQL Kubernetes operator Terraform PostgreSQL deployment PostgreSQL high availability disaster recovery PostgreSQL performance tuning query optimization PostgreSQL security IAM Zero Trust