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

Concepts

Data Analysis

Author (1):

Mike Kahn

Table of Contents (21) Chapters

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

Why subscribe?

20. Other Books You May Enjoy

Best practices for optimizing storage

After preparing and setting up your data, a good area to begin optimizing BigQuery usage is at the storage layer. BigQuery storage is relatively low cost, starting at $0.02 per GB per month (comparable to Cloud Storage or Amazon S3 storage costs) with the first 10 GB of storage per month free. When getting started with BigQuery, the first 10 GB of storage at no cost is a great advantage and creates a frictionless entry point for cost-conscious individuals and businesses. As you begin to load more data, you will want to be aware of the best practices for optimizing storage to operate efficiently and cost-effectively. In this section, we will highlight some of the best practices for managing your data storage in BigQuery.

When you create a dataset, you can set the default table expiration (refer to Figure 10.1). This allows you to set the number of days after creation that the table will be automatically deleted. This is useful for temporary data...