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Data Modeling with Snowflake

Data Modeling with Snowflake - Second Edition

By : Serge Gershkovich
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Data Modeling with Snowflake

Data Modeling with Snowflake

By: Serge Gershkovich

Overview of this book

Struggling with rising Snowflake costs and constant tuning? Poorly aligned data models can lead to bloated expenses, inefficient queries, and time-consuming rework. Data Modeling with Snowflake helps you harness the Snowflake Data Cloud’s scalable, cloud-native architecture and expansive feature set to deliver data solutions faster than ever. This book introduces simple, practical data modeling frameworks that accelerate agile design and evolve alongside your projects from concept to code. Rooted in decades of proven database design principles, these frameworks are paired, for the first time, with Snowflake-native objects and real-world examples, offering a two-in-one crash course in theory and direct application. Through real-world examples designed to make learning easy, you’ll leverage Snowflake’s innovative features like Time Travel, Zero-Copy Cloning, and Change Data Capture (CDC) to create cost-efficient solutions. Whether you're just starting out or refining your architecture, this book will guide you in designing smarter, scaling faster, and cutting costs by aligning timeless modeling principles with the power of Snowflake. *Email sign-up and proof of purchase required
Table of Contents (27 chapters)
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1
Part 1: Core Concepts in Data Modeling and Snowflake Architecture
9
Part 2: Applied Modeling from Idea to Deployment
15
Part 3: Solving Real-World Problems with Transformational Modeling
21
Part 4: Data Modeling for Enterprise Teams
23
Unlocking Value for Enterprise Organizations
24
Other Books You May Enjoy
25
Index

Modeling Slowly Changing Dimensions

In Chapter 8, Putting Conceptual Modeling into Practice, we were introduced to database facts and dimensions. While facts capture the transactions of business operations, dimensions help give those transactions meaning by providing descriptive attributes, groupings, and other contextual details. Without careful curation and maintenance of dimension tables, databases would be like 1950s police dramas (just the facts, ma’am), lacking all color and making meaningful analysis impossible.

Dimensions shed light on the nature of entities in a data model, providing details such as a customer’s billing address or a product’s description. However, entity details are constantly in flux in the fast-paced business world—customers relocate, and products gain new features. A data warehouse must be able to keep up with the steady stream of changes and allow users to quickly pivot between the latest state of the world and a historical...

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Data Modeling with Snowflake
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