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

Summary

In a DWH, fact tables present an additional challenge on top of merely capturing the latest values – they must also be able to capture and reconcile historical changes in a way that allows users to flexibly and cost-effectively query them to resolve business questions, because when it comes to operational analytics, analyzing changes, variations, and what didn’t happen can be just as valuable as the current state of truth.

Various types of fact tables exist to help an organization meet these demanding analytical needs, such as transactional, snapshot, and accumulating snapshot fact tables, among others. These fact tables must differentiate between the various kinds of measures they store (e.g., additive, semi-additive, and non-additive) because each is treated differently when updating or recording changes.

To help data teams construct and maintain these tables in Snowflake, this chapter dissected some of the toughest challenges in maintaining fact tables...