A Practical Guide from Requirements to Relational, Analytical, and NoSQL Models

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Every application is, underneath, a data model.

Screens, frameworks, and services come and go, but the tables, documents, and keys that hold your business's facts outlive them all. A good model makes features easy to add and reports easy to trust. A poor one leaks into every line of code: duplicated data that drifts, reports that disagree, and migrations nobody dares to run.

This book teaches data modeling the way you will actually use it — through real examples. Every idea is shown on concrete tables with sample rows and queries, including the common mistakes and how to fix them. One realistic running example ties it all together: Clearbrook Health, a network of outpatient clinics with appointment scheduling, a pharmacy storefront, invoices and insurance claims, multi-tenant SaaS, a data warehouse, device readings, and referral networks.

• 442 pages · 28 chapters + 4 appendices
• From requirements to conceptual, logical, and physical models
• ER diagrams, crow's-foot notation, cardinality, and optionality
• Natural vs. surrogate keys, UUIDs, and constraints
• Normalization step by step — and when to denormalize
• Many-to-many, hierarchies, polymorphism, and business rules in the schema
• Modeling time: effective dates, history, temporal data, and audit trails
• 5 full real-example domains: scheduling, orders and inventory, invoices and claims, multi-tenant permissions, flexible attributes
• Dimensional modeling: grain, facts, dimensions, star schemas, SCDs, and a complete warehouse
• Data Vault, One Big Table, and lakehouse modeling
• Document, key-value, wide-column, graph, time-series, and event models
• Zero-downtime migrations, data quality, governance, and privacy by design

Table Of Contents

Part I — Data Modeling Foundations

• Ch 1 · What Data Modeling Is and Why It Still Matters
• Ch 2 · From Requirements to Model: Finding Entities, Attributes, and Relationships
• Ch 3 · Conceptual, Logical, and Physical Models
• Ch 4 · Reading and Drawing ER Diagrams: Crow's Foot, Cardinality, and Optionality
• Ch 5 · Keys and Identity: Natural Keys, Surrogate Keys, UUIDs, and Constraints

Part II — Relational Modeling in Practice

• Ch 6 · Normalization Step by Step: From Messy Spreadsheets to BCNF
• Ch 7 · When and How to Denormalize
• Ch 8 · Modeling Relationships: Many-to-Many, Self-References, Hierarchies, and Polymorphism
• Ch 9 · Data Types, Constraints, and Business Rules in the Schema
• Ch 10 · Modeling Time: Effective Dates, History Tables, Temporal Data, and Audit Trails

Part III — Real Examples: Modeling Business Domains

• Ch 11 · Real Example: Patients, Clinicians, and Appointment Scheduling
• Ch 12 · Real Example: Orders, Inventory, and the Pharmacy Storefront
• Ch 13 · Real Example: Invoices, Payments, and Insurance Claims
• Ch 14 · Real Example: Users, Roles, Permissions, and Multi-Tenant SaaS
• Ch 15 · Real Example: Documents, Tags, and Flexible Attributes (EAV vs. JSON Columns)

Part IV — Analytical and Dimensional Modeling

• Ch 16 · OLTP vs. OLAP: Why Analytics Needs a Different Model
• Ch 17 · Dimensional Modeling: Facts, Dimensions, Grain, and Star Schemas
• Ch 18 · Slowly Changing Dimensions, Conformed Dimensions, and Bridge Tables
• Ch 19 · Real Example: The Clearbrook Clinic Operations Warehouse
• Ch 20 · Modern Analytics Modeling: Data Vault, One Big Table, and the Lakehouse

Part V — NoSQL, Events, and Integration

• Ch 21 · Document Modeling: Embedding vs. Referencing
• Ch 22 · Key-Value and Wide-Column Modeling: Designing from Access Patterns
• Ch 23 · Graph Modeling: Referrals, Networks, and Recommendations
• Ch 24 · Time-Series and Event Data: Device Readings, Logs, and Event Sourcing
• Ch 25 · Modeling for APIs and Integration: JSON Schema, Contracts, and Canonical Models

Part VI — Evolving and Governing Models

• Ch 26 · Schema Evolution and Zero-Downtime Migrations
• Ch 27 · Data Quality, Governance, Privacy, and Security by Design
• Ch 28 · Case Study: Modeling the Clearbrook Health Platform End to End

Appendices

• App A · Data Modeling Cheat Sheet
• App B · Templates and Pattern Reference
• App C · Model Review Checklist
• App D · Glossary

WHO IT IS FOR
Application developers who design tables for their features, data and analytics engineers building warehouses, analysts who write SQL every day, and tech leads who review schemas and choose between relational and NoSQL stores. Basic SQL is all you need.

FORMAT
PDF (A4), 442 pages, English, clickable table of contents and PDF bookmarks. 1st Edition, 2026. Published by thenexbooks.

This book is independent and is not affiliated with or endorsed by any database vendor. Product names are trademarks of their respective owners. Clearbrook Health and all examples are fictional; nothing in this book is medical, legal, or compliance advice.

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