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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

Modeling Facts for Rapid Analysis

Fact tables are used to store the quantitative measurements of business operations or events such as sales, employee headcounts, or website traffic. Because they contain the official record of business transactions, fact tables are a prime target for operational analytics. Fact tables aggregate metrics such as sales totals and active users, as well as historical trends (deltas), such as the margin impact of daily returns or same-day bookings before cancelations.

Because business needs vary by industry and sector, various fact table models exist to fit these different demands. Facts such as product sales and returns are erratic, while others, such as manufacturing or fulfillment, follow a predictable pattern. The fact tables supporting such processes must anticipate not only the nature of the data they aim to capture but also the organization’s analytical needs to allow for efficient reporting.

This chapter will cover the various fact table...

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Data Modeling with Snowflake
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