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

Modeling Slowly Changing Dimensions

In Chapter 7, Putting Conceptual Modeling into Practice, we were introduced to database facts and dimensions. While facts capture the transactions of business operations, dimensions help give those transactions meaning by providing descriptive attributes, groupings, and other contextual details. Without careful curation and maintenance of dimension tables, databases would be like 1950s police dramas (just the facts, ma’am), lacking all color and making meaningful analysis impossible.

Dimensions shed light on the nature of entities in a data model, providing details such as a customer’s billing address or a product’s description. However, entity details are constantly in flux in the fast-paced business world—customers relocate, and products gain new features. A data warehouse must be able to keep up with the steady stream of changes and allow users to quickly pivot between the latest state of the world and a historical...