The Semantic Layer: Building AI-Ready Data Models That Power Accurate Analytics, Trusted Dashboards, and Agentic BI

Menezes, Rodrigo; Stanton, Emma R.; Holt, Nathan

ISBN 13: 9798194531943

Published by Independently published, 2026

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Seller Inventory # I-9798194531943

Bibliographic Details

• Title: The Semantic Layer: Building AI-Ready Data Models That Power Accurate Analytics, Trusted Dashboards, and Agentic BI
• Publisher: Independently published
• Publication Date: 2026
• Language: English
• ISBN 13: 9798194531943
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Your AI Analytics Tools Are Only as Smart as the Data Beneath Them.

Power BI Copilot. Tableau Pulse. Snowflake Cortex. Databricks Genie. Every AI-powered analytics platform your organisation is evaluating runs on the same dependency: a well-governed, correctly structured semantic layer. Without it, your AI agent is a fluent guessing machine — generating confident answers from ambiguous data that no one in finance will sign off on.

The Semantic Layer is the practitioner's guide to building the data foundation that makes AI analytics trustworthy, scalable, and actually used by the people it's built for.

Written for data engineers, analytics engineers, and BI architects, this book cuts through the noise to solve the problem every data team is hitting in 2026: not that AI can't query your data — it's that AI doesn't understand what your data means.

Inside, you will learn how to:

• Define metrics once and govern them everywhere — eliminating the 17 definitions of revenue problem that erodes trust in every dashboard
- Design semantic models in dbt, Power BI, Tableau, and Looker that AI agents can reason over correctly — not just retrieve from
- Build natural language query layers that ground AI responses in certified business logic, not raw schema
- Implement row-level security, metric versioning, and access governance at the semantic layer — so AI surfaces the right data to the right people
- Prepare your data stack for agentic BI — Agent Skills, MCP connectors, and autonomous analytics workflows that depend on the semantic model you build today
- Test and validate AI query output against governed definitions, so you can prove accuracy before business users lose confidence

This is not a beginner's guide to data analytics. This is the engineering reference that senior practitioners reach for when the AI demo looked great but the production deployment fell apart — because the semantic foundation was never properly built.

Who this book is for:
Data Engineers and Analytics Engineers building on dbt, Fabric, Snowflake, or Databricks. BI Architects designing Power BI or Tableau semantic models for enterprise deployment. Data leads and CDOs driving AI analytics adoption who need a trusted, governed data layer before AI can scale.

If your organisation is investing in AI-powered analytics and needs the data models to match — this is the book that makes it work.

Scroll up and grab your copy today.