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

Scaling Data Models through Modern Techniques

After covering theory, architecture, terminology, methodology, and Snowflake-centered transformation strategies throughout the book, this chapter will build upon that foundational knowledge to address common data management challenges in large, complex environments. Specifically, this chapter will explore Data Vault 2.0 and Data Mesh methodologies—popular solutions that have emerged in response to some of the biggest challenges facing large organizations today. Despite their similar naming, Data Vault and Data Mesh attempt to tackle very different challenges, and are often used together.

Data Vault is a methodology that focuses on the efficient and flexible storage of data, with a primary focus on auditing and effortless scalability. It is made up of three pillars: modeling, methodology, and architecture. Its standardized, repeatable design patterns can be applied regardless of the complexity of the data or how many source systems...