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

Separating the model from the object

The ability to instantly scale up warehouses gives Snowflake users easy control over query performance and duration. However, increased warehouse size comes at the price of compute credits. Even keeping the warehouse size constant, changes in data volume and query patterns can cause performant and cost-effective data sources to degrade. To mitigate performance degradation, a view may need to be materialized as a table, or a table may need to become a materialized view.

However, even when converting from a view to a table, the transformational logic stays constant. While traditional modeling advice advocates differentiating views and other objects through suffixes (e.g., CUSTOMER_V), Snowflake users are encouraged to avoid such conventions. Orienting object names to their contents (e.g., CUSTOMER, DIM_DATE) rather than their object type allows modelers to easily pivot between them without breaking downstream dependencies.