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  • Book Overview & Buying Data Modeling with Snowflake
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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

Summary

In this chapter, we demonstrated that Snowflake objects pack a lot of features, even behind familiar ones such as tables and views. A table in Snowflake can store more than just the data—depending on its settings, it can also hold months of historical and disaster recovery backups and offer offset change tracking for CDC. Views, likewise, exceed expectations by providing change tracking and automated re-materialization.

We saw how stages mark the entry point for data to make its way from external sources to Snowflake tables. Stages also provide helpful features, such as external table access for reading file contents without copying them to a table beforehand.

Finally, to coordinate incoming data, establish automated ELT pipelines, and streamline CDC, Snowflake pairs tasks with streams to give its users full serverless or managed control—tying stages, tables, views, and all the connective transformational logic together.

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Programming languages
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Data Modeling with Snowflake
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