7 Data Modeling Books That Shape Your Expertise

Unlike most data modeling books that skim over practical application, this one dives deep into using Power BI to craft optimized data models tailored for business needs. Soheil Bakhshi, a Microsoft MVP with two decades in BI and data warehousing, shares his expertise through detailed chapters on managing relationships, shaping data with Power Query Editor, and employing advanced DAX functions like virtual tables and calculation groups. You’ll learn how to handle complex scenarios such as incremental refresh and row-level security, supported by real-world examples that clarify intricate concepts. This book suits BI users and analysts eager to elevate their Power BI reports by mastering efficient data modeling techniques.

Ted Hills' decades-long journey through the depths of computing, from microprocessor design to enterprise data architecture, informs this book’s unique approach to data modeling. You’ll learn how the Concept and Object Modeling Notation (COMN) bridges the gap between traditional SQL methods and emerging NoSQL technologies, allowing you to represent complex data schemas and software structures with clarity. The book walks you through practical techniques for modeling objects, concepts, and various database implementations, including key-value and columnar stores, all within a single coherent framework. If your work demands adaptability to rapidly evolving data landscapes, this book offers insights that align data semantics tightly with software design, empowering you to better manage data in modern environments.

This tailored book explores the core concepts and expert techniques of data modeling, designed specifically to match your background and goals. It covers foundational principles such as schema design and normalization, while delving into advanced applications including domain-specific modeling and performance considerations. By synthesizing collective knowledge from the field, this personalized guide focuses on your interests and challenges, offering clear explanations and examples relevant to your experience level. It reveals how data models serve as the backbone of effective databases and analytics, helping you navigate complex structures and optimize your designs.

Serge Gershkovich draws on decades of experience as a data architect to unpack Snowflake's cloud-native architecture and marry it with universal data modeling techniques. You’ll explore how Snowflake’s unique capabilities like time travel and zero-copy cloning fit into traditional frameworks such as normalization and Data Vault, transforming theoretical concepts into practical SQL recipes. This book is tailored for developers comfortable with SQL who want to deepen their understanding of effective data design and accelerate Snowflake development with proven patterns. If you’re looking for a hands-on guide that bridges enterprise-scale modeling principles with Snowflake-specific features, this book offers clear direction without unnecessary complexity.

Marco Russo and Alberto Ferrari bring decades of hands-on experience with Microsoft BI tools to this detailed manual on DAX, the formula language at the heart of Power BI, Excel, and SQL Server Analysis Services. You’ll learn to navigate complex concepts such as evaluation contexts, calculation groups, and advanced table functions, with chapters dedicated to optimizing performance and mastering variable syntax for cleaner, more maintainable code. This book suits anyone looking to deepen their understanding of business intelligence data modeling, whether you're tuning models for speed or creating sophisticated time-based calculations. Expect a thorough exploration that assumes some familiarity but rewards persistence with robust skills applicable in real-world BI projects.

What if everything you thought about Excel-based business modeling was incomplete? Wayne Winston, a Professor Emeritus at Indiana University's School of Business with extensive experience teaching Excel to Fortune 500 companies and the U.S. military, offers an in-depth guide that takes you beyond basics into sophisticated data analysis and modeling techniques. You’ll learn to harness the latest Excel features—from dynamic arrays and Power Query to Monte Carlo simulations and relational data models—to transform raw numbers into actionable business insights. This book suits analysts and decision-makers who want to leverage Excel’s full potential for forecasting, data transformation, and strategic problem solving without relying heavily on VBA programming.

This tailored book explores a focused 90-day journey designed to rapidly elevate your data modeling skills through daily, structured learning steps. It examines core concepts and advanced techniques, emphasizing hands-on practice aligned with your existing knowledge and specific interests. The content reveals how to build robust data models, understand relationships, and optimize structures effectively, all within a personalized framework that matches your background and goals. By concentrating on your unique learning path, this book transforms complex data modeling principles into manageable daily tasks, enabling steady, measurable progress over three months. It engages you deeply with critical topics such as normalization, schema design, and performance considerations, ensuring you develop practical expertise tailored precisely to your needs.

Drawing from their deep expertise at RStudio, Max Kuhn and Julia Silge crafted this guide to simplify modeling within the tidyverse ecosystem. You’ll learn how to build models from start to finish using the tidymodels framework, mastering data preparation, feature engineering, and model tuning with clear explanations of avoiding pitfalls like overfitting. Chapters break down statistical techniques for comparing and selecting models, making it especially useful if you want to translate theory into practical workflows. This book suits both newcomers seeking a structured path and experienced data scientists aiming to integrate tidyverse principles into their modeling practice.

Reza Rad challenges the common notion that Power BI issues primarily stem from complex calculations or DAX formulas, revealing instead that many problems originate in the foundational data model itself. Drawing from his extensive experience as a Power BI consultant and trainer, he lays out fundamental principles of data modeling tailored specifically for Power BI solutions, including practical examples of star schemas, dimension and fact tables. You’ll gain clarity on building a robust data model that serves as a stable base for scalable analytics, a crucial skill often overlooked until performance suffers. This book is best suited for those responsible for creating or managing Power BI implementations who want to avoid the pitfalls that arise from weak data structures.