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

The benefits of semi-structured data in Snowflake

Semi-structured data formats are popular due to their flexibility when working with dynamically varying information. Unlike relational schemas, in which a precise entity structure must be known and fixed ahead of time, semi-structured data is free to include or omit attributes as needed, as long as they are properly nested within corresponding parent objects.

Think of the contact list on your phone. It contains a list of people and their contact details but does not capture those details uniformly. For example, some contacts may contain multiple phone numbers while others have one. Some entries contain information such as an email address and street address, while others have only a number and a vague description in lieu of a name (seriously, who is Green Vespa Laura Friend, and who is Laura?).

To handle this type of data, Snowflake uses the VARIANT data type, which allows semi-structured data to be stored as a column in a relational...