Book Image

Data Modeling with Snowflake

By : Serge Gershkovich
5 (2)
Book Image

Data Modeling with Snowflake

5 (2)
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)
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 the data marts

This section will explore the Star and Snowflake schemas—popular options for architecting user-facing self-service schemas and data marts due to their efficiency and ease of understanding. Both approaches are designed to optimize the performance of data analysis by organizing data into a structure that makes it easy to query and analyze. But first, a quick overview of what a data mart is.

Data mart versus data warehouse

A data warehouse and a data mart are repositories for storing and managing data, but they differ in scope, purpose, and design. A data warehouse is a large, centralized repository of integrated data used to support decision-making and analysis across an entire organization. Data warehouses are optimized for complex queries and often use Kimball’s dimensional modeling technique or Inmon’s 3NF approach (described in his book Building the Data Warehouse). On the other hand, a data mart is a subset of a data warehouse designed...