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  • Book Overview & Buying Data Modeling with Snowflake
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Data Modeling with Snowflake

Data Modeling with Snowflake

By : Serge Gershkovich
4.6 (19)
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Data Modeling with Snowflake

Data Modeling with Snowflake

4.6 (19)
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)
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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

Transformational

It all begins with SELECT. Modeling through transformational logic is a powerful and highly maneuverable method for modeling data that comes with one serious drawback: it needs existing data to SELECT from. Transformational modeling is rarely done in transactional databases because, in such systems, data is created and modified through the company’s operational processes (e.g., purchases and profile updates)—with which expensive transformational processes should not compete for critical system resources. However, in data warehouses, where conformed datasets are extracted and duplicated with fresh timestamps for each load, transformational modeling becomes a necessity.

Because transformational modeling selects from existing structured data, the result set is already structured. Selecting the SUPERHERO_NAME and HAS_MASK columns and creating a table will preserve their structure (VARCHAR and BOOLEAN, respectively). However, as with all modeling, transformations...

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