Engineering Semantic Data Layers: Designing Enterprise Semantic Models for Business Intelligence, Analytics Engineering, AI, RAG, and Modern Data Plat
Your Data Is Growing. But Is Your Business Understanding Growing With It?
Every enterprise wants trusted Business Intelligence, reliable Enterprise AI, and a single source of truth. Yet most organizations still struggle with conflicting dashboards, duplicated business logic, inconsistent KPIs, fragmented Semantic Modeling, weak Data Governance, and AI systems that cannot reliably understand enterprise data.
If you're tired of building reports that never agree, rewriting the same business metrics across multiple tools, or wondering why your AI projects produce inconsistent results, this book was written for you.
Rather than focusing on a single vendor or platform, Engineering Semantic Data Layers teaches you how to design a production-grade Semantic Data Layer that becomes the trusted foundation for Business Intelligence, Analytics Engineering, Enterprise Data Architecture, modern Data Products, AI agents, Retrieval-Augmented Generation (RAG), and intelligent enterprise applications.
You'll learn how to engineer scalable semantic platforms that deliver governed business definitions, reusable metrics, trusted business logic, and consistent semantic models across modern cloud ecosystems. Whether you're building enterprise reporting solutions, designing AI Data Architecture, or integrating a Knowledge Graph into intelligent applications, the principles in this book remain practical, scalable, and platform-independent.
Inside this book, you'll discover how to:Design enterprise-grade Semantic Data Layers from the ground up
Master Semantic Modeling for reusable business entities, dimensions, measures, and KPIs
Build governed Data Products that serve analytics, applications, and AI consistently
Develop modern Enterprise Data Architecture using platform-neutral engineering principles
Implement robust Data Governance, metadata management, lineage, and business glossaries
Apply Analytics Engineering best practices for scalable, maintainable semantic platforms
Integrate semantic architectures with Microsoft Fabric, Power BI, Snowflake, Databricks, dbt, Tableau, Looker, and Cube
Build AI Data Architecture that supports RAG, Text-to-SQL, Model Context Protocol (MCP), Knowledge Graph solutions, and AI agents
Optimize performance, observability, CI/CD, and production operations for enterprise deployments
Modernize legacy analytics environments while preparing your organization for the future of Enterprise AI
Whether you're a Data Engineer, Analytics Engineer, BI Developer, Data Architect, Platform Engineer, AI Engineer, Solution Architect, or Technical Leader, this book provides the production-grade engineering knowledge needed to design semantic platforms that power modern data ecosystems and intelligent enterprises.
Stop treating semantics as an afterthought. Build the Semantic Data Layer that powers trusted Business Intelligence, accelerates Enterprise AI, and becomes the foundation of your organization's modern data platform.
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