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Data Modeling with Snowflake
Data Modeling with Snowflake

Data Modeling with Snowflake: A practical guide to accelerating Snowflake development using universal modeling techniques , Second Edition

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Profile Icon Serge Gershkovich
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Paperback Sep 2025 432 pages 2nd Edition
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Arrow left icon
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Data Modeling with Snowflake

Unlocking the Power of Modeling

The word modeling has come to mean very different things in the half a century that it has been practiced in database systems. This opening chapter prefaces the book’s overall aim to demystify modeling, along with its applications, methodologies, and benefits. Throughout this journey, the concept of modeling will unfold into a set of methods and terms that help organizations design and manage data and, more importantly, help them understand themselves.

In its broadest interpretation, modeling is a selective simplification that aids in navigating or designing something more complex. Any system can be broken down into smaller, more manageable pieces. Manipulating any piece individually may be straightforward, but doing so without regard to an overall strategy is a tenuous proposal that is sure to encumber scalability and maintenance down the line.

While modeling is generally considered database-agnostic, modern cloud data platforms, such...

Technical requirements

This book focuses on data modeling specifically for the Snowflake Data Cloud. While modeling includes many system-agnostic terms and conventions, this book will leverage unique features of Snowflake architecture, data types, and functions when building physical models and Structured Query Language (SQL) transformations.

To follow along with the exercises in the following chapters, you will need a Snowflake account with access to a sandbox area for creating schemas, objects, and loading data.

You can sign up for a 30-day free trial of Snowflake (https://signup.snowflake.com/) if you do not already have access.

This book will frequently use visual modeling diagrams as part of the modeling process. While a diagram can be drawn by hand and constructed in PowerPoint or Lucidchart, a tool that supports common database modeling features is recommended. The exercises in this book will take the reader from conceptual database-agnostic diagrams to deployable...

Modeling with purpose

Models are used to simplify complex systems. Take a modern city as an example; you will see that it consists of intricately linked systems such as highways, electrical grids, and transit systems. While these systems operate in the same physical territory, they require very different models to help us understand them. For example, a subway system snakes and curves below a city’s varied terrain, but our model of it—a subway map—uses straight lines and places stations at nearly equidistant intervals. The subway map is not the city—it is a selective simplification of the city that makes it easier for passengers to visualize their journey. The transit map is a model so ubiquitous that it’s hard to imagine doing it any other way—yet it took time to evolve.

The subway map, as we know it today, was invented by Harry Beck in 1931 while re-designing the map used by the London Underground. The old design was confusing to riders...

Leveraging the modeling toolkit

Before we continue, we need to formally delineate three distinct concepts often used together in the service of modeling to make it simpler to refer to a specific tool in the modeling toolkit in later sections. By understanding where each piece fits in the broader domain of database design and management, diving into deeper technical concepts later in the book will become more meaningful and easier to digest.

The three components are listed here:

  • Natural language semantics—words
  • Technical semantics—SQL
  • Visual semantics—diagrams

Let’s discuss each of these in detail, as follows:

  • Natural language semantics: Terminology employed in communicating details of a model between people. These are agreed-upon words that employ pre-defined conventions to encapsulate more complex concepts in simpler terms. For example, when both parties involved in a verbal exchange understand the concept...

The benefits of database modeling

Tactics without strategy is the noise before defeat. - Sun Tzu

For many people, database modeling brings to mind stale diagrams, arcane symbols, or extra work at the end of a project. Only a decade ago, fueled by the rise of distributed computing in the early 2000s—which popularized the concept of big data—the notion that modeling is dead gained notoriety. More precisely, it was thought that cheap and near-limitless computing power had made planning and designing a thing of the past. It was said that flexible semi-structured data formats and the ability to parse them on the fly—known as schema-on-read—had made modeling obsolete.

Eventually, operating and maintenance costs caught up with reality and revealed two great shortcomings of the schema-on-read approach. One is that no matter how data is structured, it must be functionally bound to the business that it helps support. In other words, semi-structured...

Operational and analytical modeling scenarios

The relational database as we know it today emerged in the 1970s—allowing organizations to store their data in a centralized repository instead of on individual tapes. Later that decade, Online Transaction Processing (OLTP) emerged, enabling faster access to data and unlocking new uses for databases such as booking and bank teller systems. This was a paradigm shift for databases, which evolved from data archives to operational systems.

Due to limited resources, data analysis could not be performed on the same database that ran operational processes. The need to analyze operational data gave rise, in the 1980s, to Management Information Systems (MISs), or Decision Support Systems (DSSs) as they later became known. Data would be extracted from the operational database to the DSS, where it could be analyzed according to business needs. OLTP architecture is not best suited for the latter case, so Online Analytical Processing (OLAP...

Is Snowflake limited to OLAP?

Snowflake was originally envisioned as a data warehouse—with OLAP properties to enable multidimensional analysis at scale over massive datasets. However, over time, Snowflake has released innovations that push the bounds of what is possible and often defy attempts at classification. Hybrid tables, announced in 2022, present a unique hybrid storage architecture with rowstores for OLTP transactions and columnstores for OLAP analysis in one logical object. External and Iceberg tables further expand the platform’s capabilities into the data lakehouse realm, which can’t be categorized using traditional database terms.

These terms and objects are discussed in detail in later chapters.

Although OLAP and OLTP systems are optimized for different kinds of database operations, they are still databases at heart and operate on the same set of objects (such as tables, constraints, and views) using SQL. However, each use case requires very...

Summary

There is no escaping modeling. We use it in our everyday lives to plan and navigate the complexities of the world around us—databases are no exception. For some readers, the modeling styles presented in this chapter may be a new way of conceptualizing their database landscape, while others may be getting reacquainted with its notation and use cases. Whether thinking about a company business model or sharing a finalized design with team members, we all engage in modeling to varying degrees.

Embracing database modeling and learning to speak in a commonly understood language unlocks many time-saving and collaborative benefits for the entire organization. Thinking long-term and modeling strategically, as opposed to reacting tactically, aligns database designs to the business that they underpin, ensuring their viability. Having seen the advantages that modeling uncovers and where it can be implemented, we can begin to analyze its components to understand precisely where...

Further reading

Eric Evans’ book, mentioned earlier in this chapter, explores how to create effective models by going beyond the surface and getting to the intention of the system itself. It is a recommended read for those wishing to go deeper into the realm of effective communication through models, unrestricted by specific technical domains, methods, or conventions:

Domain-Driven Design: Tackling Complexity in the Heart of Software, Eric Evans, Addison-Wesley Professional, 2004.

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Key benefits

  • Learn core modeling techniques through practical examples using native Snowflake architecture
  • Adopt a universal modeling language to communicate business value to functional teams
  • Go beyond physical modeling with SQL recipes to transform and shape your Snowflake data
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

Struggling with rising Snowflake costs and constant tuning? Poorly aligned data models can lead to bloated expenses, inefficient queries, and time-consuming rework. Data Modeling with Snowflake helps you harness the Snowflake Data Cloud’s scalable, cloud-native architecture and expansive feature set to deliver data solutions faster than ever. This book introduces simple, practical data modeling frameworks that accelerate agile design and evolve alongside your projects from concept to code. Rooted in decades of proven database design principles, these frameworks are paired, for the first time, with Snowflake-native objects and real-world examples, offering a two-in-one crash course in theory and direct application. Through real-world examples designed to make learning easy, you’ll leverage Snowflake’s innovative features like Time Travel, Zero-Copy Cloning, and Change Data Capture (CDC) to create cost-efficient solutions. Whether you're just starting out or refining your architecture, this book will guide you in designing smarter, scaling faster, and cutting costs by aligning timeless modeling principles with the power of Snowflake. *Email sign-up and proof of purchase required

Who is this book for?

Whether building data products or managing a data platform at scale, this book serves any developer who jumped into SQL without first establishing modeling best practices and understanding their effective applications to save time and effort. For analysts or those starting in database design, this book provides practical foundations of data modeling to guide you on your Snowflake data journey. Those new to Snowflake will discover its core features and learn to incorporate them into universal modeling frameworks.

What you will learn

  • Master data modeling fundamentals to get your designs right on the first attempt with time-saving benefits
  • Discover Snowflake's cloud-native architecture and unique features
  • Apply modeling concepts to achieve higher efficiency by leveraging Snowflake architecture
  • Get to grips with modeling concepts like normalization and slowly changing dimensions (SCDs)
  • Read and transform semi-structured data, including hierarchies, using pre-built recipes and examples
  • Explore organizational frameworks like Data Vault and Data Mesh
  • Understand the business metrics that drive scalable, cost-effective data projects
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Publication date : Sep 02, 2025
Length: 432 pages
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Table of Contents

26 Chapters
Core Concepts in Data Modeling and Snowflake Architecture Chevron down icon Chevron up icon
Unlocking the Power of Modeling Chevron down icon Chevron up icon
An Introduction to the Four Modeling Types Chevron down icon Chevron up icon
Mastering Snowflake’s Architecture Chevron down icon Chevron up icon
Mastering Basic Snowflake Objects Chevron down icon Chevron up icon
From Logical Concepts to Snowflake Objects Chevron down icon Chevron up icon
Mastering Advanced Snowflake Objects Chevron down icon Chevron up icon
Seeing Snowflake’s Architecture Through Modeling Notation Chevron down icon Chevron up icon
Applied Modeling from Idea to Deployment Chevron down icon Chevron up icon
Putting Conceptual Modeling into Practice Chevron down icon Chevron up icon
Putting Logical Modeling into Practice Chevron down icon Chevron up icon
Database Normalization Chevron down icon Chevron up icon
Database Naming and Structure Chevron down icon Chevron up icon
Putting Physical Modeling into Practice Chevron down icon Chevron up icon
Solving Real-World Problems with Transformational Modeling Chevron down icon Chevron up icon
Putting Transformational Modeling into Practice Chevron down icon Chevron up icon
Modeling Slowly Changing Dimensions Chevron down icon Chevron up icon
Modeling Facts for Rapid Analysis Chevron down icon Chevron up icon
Modeling Semi-Structured Data Chevron down icon Chevron up icon
Modeling Hierarchies Chevron down icon Chevron up icon
Data Modeling for Enterprise Teams Chevron down icon Chevron up icon
Scaling Data Models through Modern Techniques Chevron down icon Chevron up icon
Unlocking Value for Enterprise Organizations Chevron down icon Chevron up icon
Unlock Your Exclusive Benefits Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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