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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

Capturing the evolution of LLMs

LLMs are not mere algorithms; they are innovations, fueled by decades of research and breakthroughs. From their humble origins to today’s awe-inspiring models, LLMs have surpassed expectations, with unparalleled computational power and vast datasets. They are gateways to a new era of language processing, enabling machines to comprehend, generate, and manipulate text like never before. LLMs have evolved but they started as early as the 1950s.

The following diagram takes us through a high-level journey regarding the evolution of LLMs:

Figure 10.1: Evolution of LLMs

Figure 10.1: Evolution of LLMs

Some key research contributions have shaped the evolution of LLMs, such as the backpropagation algorithm (for training neural networks), the transformer architecture (for deriving context and meaning in sequential data), and the development of really large datasets. The evolution of LLMs is an ongoing process. As LLMs become more powerful, they will be...

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