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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

Ethical and responsible practices

In today’s world, where AI plays a pivotal role, it’s crucial to incorporate ethical practices to ensure that businesses and developers use LLMs in a way that benefits society and minimizes harm. Ethics in AI involves making responsible and morally sound choices when developing and deploying AI systems. In this section, we will discuss some of the core ethical and responsible data model design considerations for applications that use LLMs:

  • Ensure that data that’s used for training LLMs is collected and stored following ethical and legal guidelines, respecting user privacy rights. Implement robust security measures to protect sensitive data from unauthorized access:
    • It is imperative to obtain informed consent from users when collecting their data, clearly stating how their information will be used. Transparency in data collection practices builds trust with users and safeguards their privacy.
    • Regularly update data handling...

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