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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

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...

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Data Modeling with Snowflake
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