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The AI-Ready Data Architecture

Building a Semantic Layer That Works Across Your Entire Data Estate

When AI agents encounter your data, they work only with what's directly in front of them. If metric definitions are inconsistent or documentation is missing, every agent answer will be confidently wrong. This guide shows data practitioners how to build an AI Semantic Layer that spans their entire data estate on open standards, without starting over.

What's Inside

Why data access alone is not enough for AI to succeed
The three requirements of a working AI Semantic Layer
How Dremio spans every source without moving your data
A four-phase roadmap to build incrementally, without starting over

"The organizations that will get the most value from AI agents are not the ones with the most data. They are the ones with the most coherent data: meaning documented, definitions consistent, every consumer able to find the right dataset and trust its provenance."
From The AI-Ready Data Architecture

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EBOOK OVERVIEW

What You'll Learn

A practical guide for data practitioners building an AI Semantic Layer that spans their entire data estate, on open standards, without starting over.

The semantic layer problem

Access to data and understanding of data are not the same thing. Learn why the context problem and semantic silos are the unsolved piece of the AI-ready stack, and why AI agents have made it impossible to defer.

How Dremio gives agents context

Explore how a semantic layer can provide a single source of business meaning across warehouses, lakehouses, and operational systems, eliminating conflicting definitions without centralizing data.

Evolving without disruption

Follow a four-phase roadmap covering connect, define, govern, and enable that delivers measurable value at each stage, without a rip-and-replace migration or ETL overhaul.

AUTHOR

Learn about the author

Featured Author

Maeve Donovan

Senior Product Marketing Manager, Dremio

Maeve Donovan is a Senior Product Marketing Manager at Dremio, where she leads go-to-market strategy, positioning, and customer marketing. With nearly a decade of experience across Snowflake, GoodData, and Dremio, she specializes in translating complex data platform capabilities into clear business value for technical and business audiences alike. She is passionate about helping organizations cut through the noise in an increasingly crowded data infrastructure landscape.

The AI-Ready Data Architecture

Download the Ebook

What You'll Learn

The semantic layer problem

How Dremio gives agents context

Evolving without disruption

Learn about the author

Maeve Donovan

Expand Your Learning

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Semantic Layer: The Definitive Guide

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