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

Fact table measures

Numerical measures associated with a business transaction are called facts, and they fall into three basic categories:

  • Additive facts – These are measures that can be summed across any dimension. Additive facts are the most common type of fact in a DWH, allowing for a wide variety of analytical calculations and insights. These values can be aggregated across any combination of dimensions, such as time, geography, or product. Examples of additive facts include sales revenue, profit, and quantity sold.
  • Semi-additive facts – These are measures that can be summed across some dimensions but not all. These measures are usually numeric values that can only be aggregated across certain dimensions, such as customers or products. Examples of semi-additive facts include account balance and inventory levels, respectively. Semi-additive facts require special handling in data analysis to ensure that the aggregation is done correctly and does not spill...