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

Data anomalies

Bad data comes in many flavors. From misspellings to improper encoding, some data quality issues can not be avoided. However, denormalized designs make it possible to walk headlong into several well-known and preventable blunders.

To understand how normalization prevents data anomalies, we need to unpack the dual dangers it mitigates: redundancy and dependency:

  • Redundancy: Repeated data, whether within one or across multiple tables. When data values are duplicated, synchronizing everything through DML operations becomes harder.
  • Dependency: When the value of one attribute depends on the value of another. Dependencies can be functional (such as a person’s age attribute depending on their name) or multivalued (such as name, age, and hobby stored in a single table would make it impossible to delete a hobby without deleting all the people who practice it).

With these dangers in mind, let’s review the kinds of data anomalies that have the...