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Data Modeling with Snowflake

Data Modeling with Snowflake - Second Edition

By : Serge Gershkovich
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Data Modeling with Snowflake

Data Modeling with Snowflake

By: Serge Gershkovich

Overview of this book

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
Table of Contents (28 chapters)
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1
Core Concepts in Data Modeling and Snowflake Architecture
9
Applied Modeling from Idea to Deployment
15
Solving Real-World Problems with Transformational Modeling
21
Data Modeling for Enterprise Teams
23
Unlocking Value for Enterprise Organizations
25
Other Books You May Enjoy
26
Index

Data models on a spectrum of normalization

The prior sections demonstrate how, as we move up the normal form scale, redundancy and the potential for data anomalies decrease while raising the number of tables and associated complexity. With this in mind, we can visualize where various data modeling patterns fall on the normal form spectrum to help understand their suitability in design scenarios. While the dimensional modeling process was discussed in Chapter 8, Putting Conceptual Modeling into Practice, the associated schema patterns (star and snowflake) are explained in further detail in Chapter 18, Scaling Data Models Through Modern Techniques, along with Data Vault (where they are often implemented at the information mart layer). However, visualizing them in the context of normal forms will help lay the groundwork for later understanding their design. Also, bear in mind that the following assertions speak to general tendencies, not hard-set rules.

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