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

Putting Transformational Modeling into Practice

In the preceding chapters, we went from gathering requirements with business teams to creating and deploying a physical data model to Snowflake, which aligns with our organization’s operations. Now it is time to leverage Snowflake’s powerful query processing engine and its full-featured library of functions and data manipulation features to creatively transform data to answer business questions.

While physical modeling creates objects by defining the structure, transformational modeling uses logic—selecting existing data and creating a new object from the query result. However, query processing in Snowflake comes at the cost of compute credits. This chapter will cover the best practices for writing efficient queries in Snowflake to help control costs and increase performance.

As we build transformational models, we will also learn how to monitor their performance and detect issues using the Snowflake query profile...