By Serge Gershkovich (Author), Kent Graziano (Foreword).
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.
Serge Gershkovich is a seasoned data architect with decades of experience designing and maintaining enterprise-scale data warehouse platforms and reporting solutions. He is a leading subject matter expert, speaker, content creator, and Snowflake Data Superhero. Serge earned a bachelor of science degree in information systems from the State University of New York (SUNY) Stony Brook. Throughout his career, Serge has worked in model-driven development from SAP BW/HANA to dashboard design to cost-effective cloud analytics with Snowflake.
He currently serves as product success lead at SqlDBM, an online database modeling tool.
If you want to continue reading, fill out the form below to request chapter 13 access and speak with
an expert.
In this chapter, Serge reviews eight different SCD structures for meeting various analytical needs: from durable Type 0 attributes that never change to dynamic Type 7 configurations that can handle any requirement. Although many variations exist -even within SCD types-Types 1-3 are the most often used as they strike an acceptable balance between maintainability, performance, and reporting requirements.
Core Insights on SCDs:
Benefit from insights on leveraging Snowflake’s materialized views, partitioning features, and change data capture capabilities for optimal SCD handling. Whether you’re new to SCDs or seeking advanced best practices, this Chapter offers a comprehensive roadmap for success in Snowflake.
Dims track attributes for business entities that we're interested in but attributes like category and address can change over time learn how (and when) to capture attribute changes with SCDs
We'll explore type 1,2,3. Unravel the complexities of Slowly Changing Dimensions (SCDs) in Chapter 13 of Serge Gershkovich’s ‘Data Modeling with Snowflake’.
Benefit from insights on leveraging Snowflake's materialized views, partitioning features, and change data capture capabilities for optimal SCD handling. Whether you're new to SCDs or seeking advanced practices, this chapter offers a comprehensive roadmap for success in Snowflake.