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 Semi-Structured Data

So far, this book has focused on modeling structured data, the kind used in relational databases since the early 70s. However, with the rise of the internet, a different style of data became prevalent: semi-structured. Semi-structured data, such as website traffic and social media feeds, contain some organizational structure but do not conform to the formal structure of a relational database.

New file formats also emerged to support this new type of data, starting with the advent of Extensible Markup Language (XML) in the early 2000s, followed by JavaScript Object Notation (JSON), and, with the rise of distributed computing, formats such as Avro, ORC, and Parquet. These formats offered a lightweight and flexible way to structure data, making them ideal for web-based and mobile app data.

The popularity of semi-structured data can be attributed to its flexibility, adaptability, and ability to handle data sources that do not fit neatly into traditional...