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Data Exploration and Preparation with BigQuery

You're reading from  Data Exploration and Preparation with BigQuery

Product type Book
Published in Nov 2023
Publisher Packt
ISBN-13 9781805125266
Pages 264 pages
Edition 1st Edition
Languages
Author (1):
Mike Kahn Mike Kahn
Profile icon Mike Kahn
Toc

Table of Contents (21) Chapters close

Preface 1. Part 1: Introduction to BigQuery
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

Exercise and use case overview

These sample data sources outlined in the technical requirement section are representative of data sources you would use in marketing and advertising analytics. The three data sources contain jewelry store advertising, analytics, and sales data. The queries and approaches in this solution can be replicated and used for similar use cases, with actual business data. See the following diagram of the tables and some of the column associations.

Figure 11.2 – The advertising and sales datasets and their relationships

Reviewing Figure 11.2, you can see some of the possible relationships between the tables. The Ads Data and Google Analytics Data tables both have a DATE column (time and date, respectively). This can help us correlate ad keywords and site visits, possibly showing the effectiveness of advertising campaigns. The datetime column on the eCommerce Data table could then be used to determine whether an ad placement...

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