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

Schema-on-read != schema-no-need

With the rising popularity of semi-structured data, schema-on-read also entered the lexicon of big data. Schema-on-read is the idea that, unlike in relational modeling, the schema definition for semi-structured data can be delayed until long after the data has been loaded into the data platform. Delaying this task means there are no bottlenecks within the ETL process for generating and ingesting semi-structured data. However, implicit in the design is that a knowable schema exists underneath the flexible semi-structured form.

In this section, we will learn how to query JSON data and infer details about its contents using SQL and Snowflake-native functions. Let’s begin by extracting some basic attributes for our pirate:

SELECT * FROM pirate_json;

Although we can query a table containing semi-structured data in a VARIANT column, a simple SELECT * statement does not return meaningful results, as you can see in the following figure:

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