JOE REIS · BOOK 1 OF THE MIXED MODEL ARTS SERIES
The Building Blocks of
Cross-Disciplinary Data Modeling
Real-world data doesn't stay in one discipline.
Your thinking shouldn't either.
A practical data modeling book for data engineers, architects, analysts, ML/AI teams, and knowledge graph practitioners building systems across relational, analytics, application, and AI-driven data landscapes.
Also on Kindle · $29.99 · All formats and regions
Foreword by Bill Inmon
LIVE COMMUNITY SERIES
Join the community read-along for guided discussion, insights, and practical takeaways from each section of the book.
BEYOND A SINGLE SCHOOL OF THOUGHT
Every discipline has its strengths. Every discipline has its blind spots. Real-world problems don't care which camp you belong to.
Mixed Model Arts brings the major schools of data modeling into the same conversation. Learn to see what they share, where they differ, and how to draw from each with intention.
For data engineers, architects, analysts, and anyone making decisions about how data represents the world.
FROM THE FOREWORD AND THE FIELD
Foreword by Bill Inmon.
Blurbs from practitioners you already read.
FROM THE FOREWORD
“With the advent of the inclusion of different types of data being in the corporation came great complexity. The worlds of structured data and textual data were as different from each other as the Arctic North Pole is from the banks of the Amazon River. The only legitimate chance the manager of data in the corporation had to make sense of this miasma was to start to use modelling techniques.”
“AI didn't make data modeling matter more. It made the neglect visible. When agents become consumers, every vague definition starts failing at machine speed.”
“Doing data work in the AI era demands a new skill set as practitioners learn to wrangle work across broad functional domains with their coding agents. This book is arriving at the perfect time.”
“Joe was right about data engineering before it was cool, and he's right again: AI has quietly reset the rules of data modeling. He's one of the few people who reliably makes sense of this fast-moving field.”
“As I see it, Joe Reis does for data what Ranganathan did for books.”
“You can't do AI without data, and Joe Reis is one of the best people to talk to about data. He dives deep into the challenges facing data operations today, how we got here, and proposes bold but sound ideas to address them. And the best part is that he doesn’t just talk about data, but also the living organization around data, showing you how to see the system from different stakeholders’ perspectives.”
“Joe has written what I believe is the most complete book on data modeling available today, one that traverses the entire landscape from people and processes to the technical foundations. This is the book the data world has been missing.”
ON AMAZON
“I don't think it's hyperbole to say that data modeling is one of the core skills anyone who works with data on a daily basis needs to understand.”
“A great, thoroughly modern update on data modelling — why it's even more critical in our agentic world, and what to actually do about it.”
WIDEN YOUR REPERTOIRE
Explore the perspectives.
Find the connections.
STRUCTURE & INTEGRITY
Entities, relationships, keys, and constraints. Relational thinking gives you a disciplined way to express structure and protect the integrity of your data.
The connection: identity and relationships matter in every model.
MEASUREMENT & ANALYSIS
Measures, dimensions, hierarchies, aggregates, and time. Analytical modeling is the practice of defining how data is quantified, grouped, and interpreted to answer business questions—broader than dimensional modeling alone.
The connection: a metric is only useful when its context is clear.
BEHAVIOR & ACCESS
Objects, documents, events, and access patterns. Application modeling connects the shape of data to the behavior of the systems that create and consume it.
The connection: a model must serve a workload, not just describe a domain.
REPRESENTATION & LEARNING
Features, labels, embeddings, and context. ML and AI bring another set of demands to how we represent data and preserve what matters.
The connection: every representation makes assumptions about the world.
MEANING & RELATIONSHIPS
Concepts, relationships, and shared meaning. Knowledge graphs make connections explicit so that information can be interpreted across boundaries.
The connection: structure alone does not communicate meaning.
BUILD YOUR FOUNDATION
Entities, attributes, and relationships. The choices that shape how a system sees the world.
Grain, time, and meaning. Make the assumptions behind a model explicit.
Organizational realities, trade-offs, and the ongoing work of keeping models useful.
LOOK INSIDE
Page count, ISBNs,
and the table of contents.
TABLE OF CONTENTS
THE SERIES
Mixed Model Arts is the conceptual and practical framework — the building blocks. Book 2, Data Modeling Across Use Cases, applies those blocks to operational, analytics, and ML/AI work. It is in progress. Subscribe to Practical Data Modeling to hear when it's ready.
PUT IT INTO PRACTICE
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$49.99
474 pages. 6 × 9 inches. ISBN 979-8-95247-800-8. A copy for your desk, your bag, and your next difficult modeling conversation.
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