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Mixed Model Arts: The Building Blocks of Cross-Disciplinary Data Modeling
In 1993, the Ultimate Fighting Championship settled an old argument.
Every martial art claimed to have the answer. Then practitioners from different disciplines competed directly.
No single style had all the answers.
The fighters who came to dominate learned across disciplines and used what worked.
Data modeling is having its UFC moment.
For decades, data modeling has evolved into five broad relational, analytics, applications, ML/AI, and knowledge. Each developed powerful techniques for the problems it was trying to solve. Each also developed its own vocabulary, assumptions, and blind spots. Meanwhile, real-world data stopped respecting those boundaries and moved on.
Your product catalog lives in JSON. Your analytics dashboard disagrees with your recommendation engine. Your knowledge graph describes concepts that your operational database represents differently. AI agents now read across documents, metrics, metadata, embeddings, and operational systems.
No single camp has all the answers.
Enter Mixed Model Arts .
Written by Joe Reis, co-author of Fundamentals of Data Engineering, Mixed Model Arts takes a pragmatic approach to modern data learn from every tradition, understand the fundamental concepts beneath them, and apply the techniques that fit the problem in front of you.
Featuring a foreword by Bill Inmon, the father of data warehousing.
Inside, you’ll learn how Work across the five camps of data relational, analytics, applications, ML/AI, and knowledgeModel the five forms of structured, semi-structured, metadata, unstructured, and ML/AI artifactsUse concepts that cut across every entities, identifiers, attributes, relationships, grain, time, and meaningUnderstand one of the most important (and most frequently misunderstood) concepts in data modelingModel data for AI systems and so machines can work with data in context rather than merely retrieve itModel the business instead of simply reproducing source systemsNavigate organizational including the incentives, politics, and communication failures that derail modeling effortsWho this book is forData and analytics engineers trying to understand inherited schemas nobody can explainBackend developers designing data structures that need to survive beyond the next featureArchitects and staff engineers creating shared structure across teams and systemsData leaders dealing with systems that disagree about the same businessML and AI practitioners discovering that many AI problems are really data problems underneathThe book is light on code and heavy on reasoning. Read it straight through, or follow the reading paths in the front matter based on what you need.
Every chapter ends with a “Try This” exercise because modeling is something you practice, not something you memorize.
Mixed Model Book One
The real work happens in the gym
Every martial art claimed to have the answer. Then practitioners from different disciplines competed directly.
No single style had all the answers.
The fighters who came to dominate learned across disciplines and used what worked.
Data modeling is having its UFC moment.
For decades, data modeling has evolved into five broad relational, analytics, applications, ML/AI, and knowledge. Each developed powerful techniques for the problems it was trying to solve. Each also developed its own vocabulary, assumptions, and blind spots. Meanwhile, real-world data stopped respecting those boundaries and moved on.
Your product catalog lives in JSON. Your analytics dashboard disagrees with your recommendation engine. Your knowledge graph describes concepts that your operational database represents differently. AI agents now read across documents, metrics, metadata, embeddings, and operational systems.
No single camp has all the answers.
Enter Mixed Model Arts .
Written by Joe Reis, co-author of Fundamentals of Data Engineering, Mixed Model Arts takes a pragmatic approach to modern data learn from every tradition, understand the fundamental concepts beneath them, and apply the techniques that fit the problem in front of you.
Featuring a foreword by Bill Inmon, the father of data warehousing.
Inside, you’ll learn how Work across the five camps of data relational, analytics, applications, ML/AI, and knowledgeModel the five forms of structured, semi-structured, metadata, unstructured, and ML/AI artifactsUse concepts that cut across every entities, identifiers, attributes, relationships, grain, time, and meaningUnderstand one of the most important (and most frequently misunderstood) concepts in data modelingModel data for AI systems and so machines can work with data in context rather than merely retrieve itModel the business instead of simply reproducing source systemsNavigate organizational including the incentives, politics, and communication failures that derail modeling effortsWho this book is forData and analytics engineers trying to understand inherited schemas nobody can explainBackend developers designing data structures that need to survive beyond the next featureArchitects and staff engineers creating shared structure across teams and systemsData leaders dealing with systems that disagree about the same businessML and AI practitioners discovering that many AI problems are really data problems underneathThe book is light on code and heavy on reasoning. Read it straight through, or follow the reading paths in the front matter based on what you need.
Every chapter ends with a “Try This” exercise because modeling is something you practice, not something you memorize.
Mixed Model Book One
The real work happens in the gym
474 pages, Paperback
Published September 27, 2026
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