AI-Ready Data: Designing Data Platforms for LLMs, Agents, and RAG (Merced Books on Agentic AI and Data)

Most AI projects fail because of bad data, not bad models.

Organizations spend billions on AI infrastructure, yet the majority of AI projects stall before reaching production. The root cause is rarely the model. It is the data: wrong formats, missing context, poor quality, ungoverned access. This book teaches you how to build data platforms that produce high-quality inputs for LLMs, RAG pipelines, and agentic systems.

AI-Ready Data is a practical guide for data engineers, platform architects, and AI builders who need to bridge the gap between data engineering and AI engineering. Written by Alex Merced, Head of Developer Relations at Dremio and one of the leading voices in lakehouse architecture, it covers the full data stack that AI systems depend on.

What you will learn:

Define what AI-ready means across the dimensions of structure, quality, accessibility, and governance Implement data quality frameworks specific to AI workloads: completeness, consistency, freshness, and bias detection Structure data for RAG with intelligent chunking, metadata enrichment, and document processing pipelines Build embedding and vector infrastructure with model selection, index management, and update strategies Design semantic layers that give AI agents the vocabulary to query structured data accurately through Dremio Build knowledge graphs that capture entity relationships and provide structured context for AI reasoning Implement feature stores for real-time and batch feature serving with point-in-time correctness Architect open lakehouse platforms on Apache Iceberg for unified analytics and AI workloads Design AI data pipelines with incremental processing, quality gates, and feedback loops Implement data governance for AI with role-based access control, PII management, and compliance automation Build observability for AI data with freshness SLAs, drift detection, and data contracts Includes 24 professional diagrams illustrating data architectures, pipeline patterns, and infrastructure designs.

For data engineers: Build the data infrastructure that AI teams actually need.

For AI engineers: Understand the data layer that determines whether your AI systems work.

For data architects: Design platforms that serve both analytics and AI on a single architecture.