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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

Summary of operational aspects and design considerations

When you are evaluating a cloud data warehouse or analytical storage for your application, make sure you design for the following key operational aspects:

  • Data warehouse migration: Ensure the choice and configuration for your data warehouse supports easy migration of your data to the cloud data warehouse in case your application requires it.
  • Transferring data: Consider the need to efficiently move and synchronize data between different systems and platforms. The format and way you ingest data into BigQuery is an important aspect, as we discussed earlier.
  • Data governance and security options: Make sure your choice of data warehouse or analytics system allows you to implement robust security measures and governance practices to protect your data.
  • Real-time and predictive analytics: While designing an analytics solution, also consider enabling real-time data processing and predictive analytics for actionable...
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