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Database Design and Modeling with Google Cloud

You're reading from   Database Design and Modeling with Google Cloud Learn database design and development to take your data to applications, analytics, and AI

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Product type Paperback
Published in Dec 2023
Publisher Packt
ISBN-13 9781804611456
Length 234 pages
Edition 1st Edition
Concepts
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Author (1):
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Abirami Sukumaran Abirami Sukumaran
Author Profile Icon Abirami Sukumaran
Abirami Sukumaran
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Table of Contents (18) Chapters Close

Preface 1. Part 1:Database Model: Business and Technical Design Considerations
2. Chapter 1: Data, Databases, and Design FREE CHAPTER 3. Chapter 2: Handling Data on the Cloud 4. Part 2:Structured Data
5. Chapter 3: Database Modeling for Structured Data 6. Chapter 4: Setting Up a Fully Managed RDBMS 7. Chapter 5: Designing an Analytical Data Warehouse 8. Part 3:Semi-Structured, Unstructured Data, and NoSQL Design
9. Chapter 6: Designing for Semi-Structured Data 10. Chapter 7: Unstructured Data Management 11. Part 4:DevOps and Databases
12. Chapter 8: DevOps and Databases 13. Part 5:Data to AI
14. Chapter 9: Data to AI – Modeling Your Databases for Analytics and ML 15. Chapter 10: Looking Ahead – Designing for LLM Applications 16. Index 17. Other Books You May Enjoy

Data model design considerations for applications that use LLMs

The best-suited data modeling techniques and principles for data model design for applications that use LLMs will vary depending on the specific application. However, some general considerations include (but are not limited to) the following:

  • The type of data that will be used: The data modeling technique that is chosen will need to be able to represent the different types of data that will be used in the application.
  • The scalability of the app: The data modeling technique that is chosen will need to be able to scale as the app grows. For example, if the app is expected to have a large number of users and growing attributes, then a NoSQL database may be a better choice than a relational database.
  • Data security and privacy: The data modeling technique that is chosen will need to be able to protect the data from unauthorized access. For example, the data may need to be encrypted or stored in a secure location...
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