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

Summary

In this chapter, we demonstrated that Snowflake objects pack a lot of features, even behind familiar ones like tables and views. A table in Snowflake can store more than just the data—depending on its settings, it can also hold months of historical and disaster recovery backups and offer offset change tracking for CDC. Views, likewise, exceed expectations by providing change tracking and automated re-materialization.

We saw how stages mark the entry point for data to make its way from external sources to Snowflake tables. Stages also provide helpful features, such as external table access for reading file contents without copying them to a table beforehand.

Finally, to coordinate incoming data, establish automated ELT pipelines, and streamline CDC, Snowflake pairs tasks with streams to give its users full serverless or managed control—tying stages, tables, views, and all the connective transformational logic together.

Having understood the strengths and...