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Database Design and Modeling with Google Cloud

Database Design and Modeling with Google Cloud

By : Sukumaran
4.9 (7)
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Database Design and Modeling with Google Cloud

Database Design and Modeling with Google Cloud

4.9 (7)
By: Sukumaran

Overview of this book

In the age of lightning-speed delivery, customers want everything developed, built, and delivered at high speed and at scale. Knowledge, design, and choice of database is critical in that journey, but there is no one-size-fits-all solution. This book serves as a comprehensive and practical guide for data professionals who want to design and model their databases efficiently. The book begins by taking you through business, technical, and design considerations for databases. Next, it takes you on an immersive structured database deep dive for both transactional and analytical real-world use cases using Cloud SQL, Spanner, and BigQuery. As you progress, you’ll explore semi-structured and unstructured database considerations with practical applications using Firestore, cloud storage, and more. You’ll also find insights into operational considerations for databases and the database design journey for taking your data to AI with Vertex AI APIs and generative AI examples. By the end of this book, you will be well-versed in designing and modeling data and databases for your applications using Google Cloud.
Table of Contents (18 chapters)
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1
Part 1:Database Model: Business and Technical Design Considerations
4
Part 2:Structured Data
8
Part 3:Semi-Structured, Unstructured Data, and NoSQL Design
11
Part 4:DevOps and Databases
13
Part 5:Data to AI

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

AI data modeling

considerations 160

analytical applications 30

examples 30

analytical data modeling

considerations 160

API, enabling in Google Cloud project on Vertex AI

reference link 189

API endpoint and method 104

APIs

enabling, in GCP Console 116

applications of data, on cloud 21

analytics and insights 21

backup and disaster recovery 21

development 21

storage 21

user experience and personalization 22

archival storage 120

artificial intelligence (AI) 4

Atomicity, Consistency, Isolation, and Durability (ACID) properties 31, 48, 93

automatic scaling 142

automatic updates 134

availability 93

B

backup plan 134

big data 21

BigQuery 72, 115

dataset, creating 75, 76

data, streaming into 82

enabling, from console...

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