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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

Unstructured Data Management

Unstructured data is said to constitute a huge portion of data generated today. Some industry studies say at least 85% of data available today is unstructured. So, what is this unstructured data? Data with no predefined external structure in the form of a schema or table (rows and columns), object structure (JSON, XML, and so on), or data model is termed unstructured data, even though such data can have an internal structure. It can be generated by machines, humans, or applications.

Some common examples and sources of unstructured data are images, audio, video, files, and rich media. The fact that it is not constrained by fixed schema and is flexible for analyzing and drawing insights from raw data as-is is one of the major advantages of this type of data management. However, industries and organizations are not able to take full advantage of data of this kind for many reasons:

  • The complexity involved in terms of data storage, management, processing...

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