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

Traditional architectures

To appreciate the innovation of the Snowflake Data Cloud, we have to take a step back and recall the designs and related limitations associated with its predecessors. Long before the advent of the cloud, databases started as physical on-premises appliances and, since their inception, have all faced the same challenge: scalability.

In the past, databases were confined to a physical server on which they relied for storage and processing power. As usage increased, memory would fill up, and CPU demand would reach the available limit, forcing the user to add more resources to the server or buy a new one altogether. As either response involved maintenance and downtime, hardware purchases had to be forward-looking, anticipating database growth several years into the future.

The following figure outlines the structure and key pieces of a traditional database. Although processing power, memory, and disk space were all customizable to a degree, they came packaged...