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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

BigQuery best practices and cost management

In this section, we will go over general best practices for using BigQuery. We will also touch on cost management and the different approaches to optimizing your spending on BigQuery. By the end of this section, you will have a good understanding of how to efficiently use BigQuery to control costs and optimize storage and queries.

Best practices

There are many best practices for using BigQuery. The following are a few best practices that are foundational for using this service efficiently. Take note and implement these approaches to get the most performance and benefit out of BigQuery:

  • Use the right data type for your data: BigQuery supports many data types, each with its own storage cost. Keep in mind that a large string column takes up more space than an integer column. When designing your table schema, make sure to choose the right data type for your data.
  • Query only the columns you need: Avoid using SELECT * as this...
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