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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

Significance of ETL in data warehouse

ETL is a process in data warehousing that represents extract, transform, and load. This process involves extracting data from multiple sources, transforming and performing computations, cleansing for data quality, and loading the data into a target system. ETL is important for data warehouses to collect, read, process, transform, migrate, and analyze data from several disparate sources into one target database or warehouse. ETL eliminates silos in sources and integrates data for easy access and BI.

The ETL process typically consists of the following steps:

  • Extract/ingest: Data is extracted from the source systems. This can be done using a variety of methods, such as database queries, file transfers, or APIs.
  • Transform: The data is transformed into a format that is compatible with the target system. This may involve cleaning the data, converting data types, or merging data from multiple sources.
  • Load: The data is loaded into the...

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