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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

Putting Logical Modeling into Practice

In the previous chapter, we observed data teams working with business teams to create a high-level conceptual model representing an organization’s main entities and relationships. While a conceptual model helps to understand the overall structure and requirements of the data without going into excessive detail, the next stage in the modeling process requires us to go further and develop a detailed model to be used as a blueprint for moving to a physical database design.

To complete the logical model, the data team will have to collaborate with domain experts from the business once again to expand the list of entities, attributes, and relationships that will be used in the database, as well as the data types and constraints for each element. Just as the conceptual model held bidirectional benefits in both developing a fresh design and simplifying an existing one, a logical model is not merely a stepping stone in the modeling process.

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