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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

Data to AI

This section is a perspective on data modeling for journeying from data to AI through several stages, including ingestion to storage, integration, transformation, and archival considerations:

  1. Data ingestion: Data ingestion is the process of acquiring and importing data from various sources into an analytics database or data warehouse. When designing the data model for ingestion, consider the frequency and volume of data updates, data formats, and data integration requirements. Choose appropriate ingestion mechanisms such as batch processing, real-time streaming, or event-based ingestion based on the timeliness and velocity of your data. Ensure data validation and cleansing mechanisms are in place to maintain data quality during ingestion.
  2. Storage: Choosing the right storage infrastructure is crucial for efficiently managing and accessing large volumes of data in AI workflows. Cloud object storage and database services such as Google Cloud Storage and BigQuery...

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