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

Summary

The exercises in this chapter demonstrate what is required to transform a logical model into a deployable, physical design. However, before such transformation occurs, each project’s use case should be carefully considered. As there is no one-size-fits-all guideline for Snowflake databases, decisions must be made considering performance, cost, data integrity, security, and usability. However, unlike traditional databases, long-standing issues such as backup, recovery, and scalability are handled by Snowflake features and architecture.

Once the physical properties have been decided, users create physical equivalents of all logical objects, including many-to-many and subtype/supertype relationships, yielding a final set of physical tables. Following this, naming standards, database objects, columns, and their relationships are declared before deploying the resulting model.

Deployable Snowflake DDL code is produced from an ERD through a process called forward engineering...