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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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Toc

Table of Contents (21) Chapters Close

Preface 1. Part 1: Introduction to BigQuery
2. Chapter 1: Introducing BigQuery and Its Components FREE CHAPTER 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

Summary

Designing and organizing your BigQuery data is an important part of using BigQuery effectively. In this chapter, we outlined Google Cloud’s resource hierarchy and BigQuery resources and provided best practices for each feature. This chapter’s diagrams were built upon as the chapter progressed, providing a visual approach to understanding BigQuery resources, design, and organization.

In conclusion, designing and organizing a BigQuery environment requires a good understanding of resources and consideration of several key factors, including pattern and schema design. By following the best practices in this chapter, you can create a data warehouse that is easy to query and analyze, cost-effective, and performant. In the next chapter, we will shift to exploring data. Read on to learn about the various tools and approaches to continue to build your data analysis skillset.

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