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

With the rising popularity of web applications and IoT data, semi-structured data has gained prominence for its flexibility in creating and loading dynamically changing objects without affecting ELT pipelines. Semi-structured formats, such as JSON, can handle any amount of variable nested data, which doesn’t need to conform to a pre-defined structure. Snowflake makes working with semi-structured formats easy thanks to its VARIANT data type – optimized for storage and analytical queries using easy-to-learn extensions to ANSI-standard SQL.

Querying a VARIANT data type provides the same performance as standard relational data types without needing to analyze the structure ahead of time – an approach known as schema-on-read. This means Snowflake users can work with semi-structured and relational data on the same platform using familiar SQL commands. However, although Snowflake gives users all the tools necessary for analyzing semi-structured data, schema...