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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

Databases

That’s right! The set of application programs that store, access, manage, and update this data while dealing with structure, recovery, security, privacy, concurrency, and more, and attribute comprehensively to getting the day in the life of a modern-day human done right, is the database. It is also called the database management system or DBMS.

A teeny-tiny bit about the evolution of databases

Long before the term data was even coined, humans used the Ishango bone (what is assumed to be a notched baboon bone) as a tally stick speculated to have some mathematical engravings or even something of astrological relevance. Dating to 20,000 years before the present, it is regarded as the oldest mathematical “database” (logging numerical information for future use) tool for humankind, with the possible exception of the approximately 40,000-year-old Lebombo bone from southern Africa. Then, we have Acharya Pingala from the third to second century BC who first described the binary number system that lives on today – forming the foundations of any computing there is, including database systems. Slowly and steadily, we progressed into advanced computing, databases, and technology in general with calculators, computers, automation, wartime wonders, relational database management, the internet, Google Search (yes, it has come to be identified as an important event in the evolution of technology), artificial intelligence (AI), machine learning (ML), and big data.

Isn’t it fascinating how everything important dates back to monkeys or monkey bones, just like computers and homo sapiens themselves?

DBMS

Exactly 52 years ago, E.F. Codd, the father of DBMSs, propounded and formalized the 12 commandments, of which there are 13 (starting from 0. I know, right?), that make up a DBMS. You can read about it here: https://en.wikipedia.org/wiki/Codd%27s_12_rules. We have evolved since the 1960s, when we used one database to store and secure information, to modern times, where we use one database per stage in the data life cycle – that is, one database per data stage, type, and structure in most cases. We will dive deeper into each of these categories throughout this book with examples and exercises, so don’t panic if this is a jargon overdose at this point.

In this chapter, we are going to discuss the business attributes, technical aspects, design questions, and considerations to keep in mind while designing a database model.

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