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

Saving cash by using cache

With on-premises databases, inefficient operations resulted in longer execution times. In Snowflake’s variable spend model, that extra time is coupled with monetary penalties. Besides writing efficient SQL, Snowflake users should also understand the various caches associated with the service and virtual compute layers to understand where they can take advantage of pre-calculated results. A firm grasp of Snowflake caching will also inform decisions when modeling and building data pipelines.

Let us start with the services layer and familiarize ourselves with the caches it manages and offers its users.

Services layer

The services layer handles two types of cache: metadata and query results cache.

Metadata cache

The services layer manages object metadata, such as structure, row counts, and distinct values by column. Reviewing this metadata through related SQL functions or the Snowflake UI will not require a running warehouse and does not...