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Data Exploration and Preparation with BigQuery

You're reading from   Data Exploration and Preparation with BigQuery A practical guide to cleaning, transforming, and analyzing data for business insights

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Product type Paperback
Published in Nov 2023
Publisher Packt
ISBN-13 9781805125266
Length 264 pages
Edition 1st Edition
Languages
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Author (1):
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Mike Kahn Mike Kahn
Author Profile Icon Mike Kahn
Mike Kahn
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Introduction to BigQuery FREE CHAPTER
2. Chapter 1: Introducing BigQuery and Its Components 3. Chapter 2: BigQuery Organization and Design 4. Part 2: Data Exploration with BigQuery
5. Chapter 3: Exploring Data in BigQuery 6. Chapter 4: Loading and Transforming Data 7. Chapter 5: Querying BigQuery Data 8. Chapter 6: Exploring Data with Notebooks 9. Chapter 7: Further Exploring and Visualizing Data 10. Part 3: Data Preparation with BigQuery
11. Chapter 8: An Overview of Data Preparation Tools 12. Chapter 9: Cleansing and Transforming Data 13. Chapter 10: Best Practices for Data Preparation, Optimization, and Cost Control 14. Part 4: Hands-On and Conclusion
15. Chapter 11: Hands-On Exercise – Analyzing Advertising Data 16. Chapter 12: Hands-On Exercise – Analyzing Transportation Data 17. Chapter 13: Hands-On Exercise – Analyzing Customer Support Data 18. Chapter 14: Summary and Future Directions 19. Index 20. Other Books You May Enjoy

Exploring architecture patterns

This section presents three architecture patterns for organizing BigQuery resources. The following three patterns should cover most of the common usage patterns for teams and organizations using BigQuery. It is important to understand these architecture patterns to determine what setup in BigQuery will work best for your usage. Each pattern has its advantages and most organizations combine elements of multiple patterns. We will lead with the recommended design and outline alternative approaches, including the considerations for each one:

  • Centralized enterprise data warehouse: The BI and data team owns all department or business unit data. Departments have their own projects with views and dashboards for utilizing the organization’s enterprise data warehouse.
  • Decentralized enterprise data warehouse: Each department owns its raw data. The organization creates a central data warehouse project for analysis.
  • Cross-org data exchange...
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