Book Image

Data Modeling with Snowflake

By : Serge Gershkovich
5 (2)
Book Image

Data Modeling with Snowflake

5 (2)
By: Serge Gershkovich

Overview of this book

The Snowflake Data Cloud is one of the fastest-growing platforms for data warehousing and application workloads. Snowflake's scalable, cloud-native architecture and expansive set of features and objects enables you to deliver data solutions quicker than ever before. Yet, we must ensure that these solutions are developed using recommended design patterns and accompanied by documentation that’s easily accessible to everyone in the organization. This book will help you get familiar with simple and practical data modeling frameworks that accelerate agile design and evolve with the project from concept to code. These universal principles have helped guide database design for decades, and this book pairs them with unique Snowflake-native objects and examples like never before – giving you a two-for-one crash course in theory as well as direct application. By the end of this Snowflake book, you’ll have learned how to leverage Snowflake’s innovative features, such as time travel, zero-copy cloning, and change-data-capture, to create cost-effective, efficient designs through time-tested modeling principles that are easily digestible when coupled with real-world examples.
Table of Contents (24 chapters)
1
Part 1: Core Concepts in Data Modeling and Snowflake Architecture
8
Part 2: Applied Modeling from Idea to Deployment
14
Part 3: Solving Real-World Problems with Transformational Modeling

Database Normalization

Previous chapters have explored the method of capturing the real-world business workings of an organization and modeling them using visual semantics. The resulting model and accompanying diagrams make it easy for the domain and data teams to reach a consensus about the business’s fundamental entities and the interactions between them. However, as the modeling process approaches the physical stage, we should understand that many ways exist to structure the data at the database level. The process of dividing the data into smaller, modularized segments is known as normalization. In this chapter, we will understand how it works and the advantages and disadvantages that go along with it.

Normalization is not a binary classification but a spectrum of ever-increasing rules a design must satisfy to achieve a stated level. While normalization contains the root word normal, along with the obvious positive connotations, more normalization does not necessarily...