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

In this chapter, we continued the modeling journey by taking the conceptual design from the previous chapter and expanding it to logical by adding structural details and technical details that closely resemble the physical model we will create and deploy in later chapters.

Continuing to work with business experts from our organization, we identified the attributes that will be used to capture crucial business master data and transactions. We defined each business entity’s identifiers, attributes and measures, and data types to do this.

Once attributes have been set in place, we reviewed the relationships between entities to understand the nuances of their associations to architect them in such a way as to fit the needs of the business and simplify their maintenance.

We started by identifying the M:M relationships. These relationships require an associative table to capture the interactions between the entities involved but are not considered entities themselves...