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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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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
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24
Other Books You May Enjoy
25
Index

Misconceptions

Before we can begin planning and justifying data modeling efforts, it’s important to dismiss some common misconceptions that can hinder these initiatives from starting. Unlike the barriers we will cover later in this chapter, which require some actions to overcome, misconceptions can often be dispelled outright if we are prepared to address them. Below are some misconceptions about data modeling that you might encounter, typically framed as questions from senior decision-makers when reviewing a data modeling proposal. Let’s ensure we have the correct responses ready when they do.

Data modeling is just for the data team

The misconception that data modeling is a technical activity with limited application outside the data team likely stems from the legacy tooling that data modeling has been confined to for decades. When the data model exists solely in specialized software on the desktop of the data architect and can, at best, be viewed in static...

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