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

Considerations before starting the implementation

When transitioning from a conceptual or logical design, where entities, attributes, relationships, and additional context have already been defined, there appears to be little to do at first glance when moving to a physical model. However, the specifics of Snowflake’s unique cloud architecture (discussed in Chapters 3 and 4), from its variable-spend pricing to time-travel data retention, leave several factors to consider before embarking on physical design. We’ll cover these factors in the following sections.

Performance

Query performance in Snowflake is heavily dependent on the clustering depth of the micro-partitions, which, in turn, are influenced by the natural sort order of the data inserted. Apart from Hybrid Unistore tables, which allow users to enable indexes, there are few performance tuning options left to the user besides sorting data before inserting and clustering. If the data volume in a given table...