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Amazon DynamoDB - The Definitive Guide

You're reading from   Amazon DynamoDB - The Definitive Guide Explore enterprise-ready, serverless NoSQL with predictable, scalable performance

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781803246895
Length 414 pages
Edition 1st Edition
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Author (1):
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Aman Dhingra Aman Dhingra
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Aman Dhingra
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Table of Contents (24) Chapters Close

Preface 1. Part 1:Introduction and Setup
2. Chapter 1: Amazon DynamoDB in Action FREE CHAPTER 3. Chapter 2: The AWS Management Console and SDKs 4. Chapter 3: NoSQL Workbench for DynamoDB 5. Part 2: Core Data Modeling
6. Chapter 4: Simple Key-Value 7. Chapter 5: Moving from a Relational Mindset 8. Chapter 6: Read Consistency, Operations, and Transactions 9. Chapter 7: Vertical Partitioning 10. Chapter 8: Secondary Indexes 11. Part 3: Table Management and Internal Architecture
12. Chapter 9: Capacity Modes and Table Classes 13. Chapter 10: Request Routers, Storage Nodes, and Other Core Components 14. Part 4: Advanced Data Management and Caching
15. Chapter 11: Backup, Restore, and More 16. Chapter 12: Streams and TTL 17. Chapter 13: Global Tables 18. Chapter 14: DynamoDB Accelerator (DAX) and Caching with DynamoDB 19. Part 5: Analytical Use Cases and Migrations
20. Chapter 15: Enhanced Analytical Patterns 21. Chapter 16: Migrations 22. Index 23. Other Books You May Enjoy

Breaking down your data

This section of the chapter, like the others, assumes you are relatively new to NoSQL data modeling and may have some proficiency with modeling databases in RDBMS. Going from the RDBMS-based model to a NoSQL-based one, you may need to move your mindset from normalization to denormalization with potentially some duplication as well; however, the denormalization here could itself be done cleverly. Simply dumping all the columns/attributes into a single large JSON blob and retrieving that blob for any data access request that may not warrant reading the 100 other attributes within the blob is not quite what would work. If it does, that would not be efficient anyway.

This section will showcase a few design patterns and thought-provoking concepts that would help you think about the denormalization of data coming from the relational world itself, but also doing it cleverly, by identifying opportunities to break up the data as per the data access patterns, to be...

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