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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

Adding attributes

The most important detail in a dimension is its unique identifier. This determines what constitutes a unique instance of each entity in our business. Examples of unique identifiers include things such as serial numbers for parts and employee IDs for a company’s human capital. Domain experts from each business area can confirm these and other necessary details. We will use the CUSTOMER dimension as an example to identify the relevant details and incorporate them into the model.

Suppose we sat down with the head of the sales team to learn about how our organization identifies customers and their relevant attributes. The domain expert explains that besides typical customer attributes such as name and address, our organization is also interested in tracking the account balance and identifying its market segment. The sales team also explains how customers are grouped into regions based on their NATION attribute, maintained in a separate LOCATION dimension. Besides...

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Data Modeling with Snowflake
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