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

Due to the constantly changing nature of master data in the source system, the data warehouse must serve two critical functions to allow business users to pivot between current and historical attribute values in their reporting. These functions consist of capturing source system changes in a landing area and creating SCDs that meet the organization’s reporting needs. Because master data plays such a key part in organizational analytics—often being tracked and scrutinized independently of fact records—learning to construct the required SCD structures and load them efficiently is a fundamental task for any data warehouse team.

In this chapter, we reviewed eight different SCD structures for meeting various analytical needs: from durable Type 0 attributes that never change to dynamic Type 7 configurations that can handle any requirement. Although many variations exist—even within SCD types—Types 1-3 are the most often used as they strike an...