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 enable users to deliver data solutions quicker than ever before.
However, unless modeling best-practices are observed, the costs will quickly pile up. When the data and the business model don’t match, or designs fail to leverage platform efficiencies, constant tuning and re-work will result in elevated operational and human capital costs.
This book will familiarize the reader with simple and practical data modeling frameworks which accelerate agile design and evolve with the project from concept to code. These universal principles have been guiding database design for decades and, in this book, are coupled for the first time with unique Snowflake-native objects and examples—giving the reader a two-for-one crash course in the theory and its direct application.
Readers will learn how to leverage Snowflake’s innovative features like “Time Travel”, “Zero-copy Cloning”, and change-data-capture to create cost-saving, efficient designs through time-tested modeling principles that are easily digestible when coupled with real-world examples.

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Spis treści

Data Modeling with Snowflake. A practical guide to accelerating Snowflake development using universal modeling techniques - Second Edition eBook -- spis treści

• 1. Unlocking the power of modeling
• 2. The four-step modeling process
• 3. Mastering Snowflake features
• 4. Mastering Fundamental Snowflake Objects
• 5. Mastering Advanced Snowflake Objects
• 6. Speaking modeling through Snowflake objects
• 7. Seeing Snowflake architecture through modeling notation
• 8. Conceptual modeling - putting it into practice
• 9. Logical modeling- putting it into practice
• 10. Normal forms - pros, cons, and the two that matter
• 11. Structuring a Snowflake database like a pro
• 12. Physical modeling - putting it into practice
• 13. Transformational modeling - putting it into practice
• 14. Modeling slowly changing dimensions
• 15. When the facts change - historifying transactional data
• 16. Structuring Semistructured data
• 17. Taming hierarchies
• 18. Scaling Data Models through Modern Techniques
• 19. Scaling Enterprise Organizations Through Data Modeling
• 20. Appendix