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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

Using functions

When working with BigQuery, one of the most powerful features is your ability to leverage various functions to manipulate and analyze your data. Functions allow you to perform calculations, transformations, aggregations, and more with your SQL queries. This section explores the different types of functions available and shows how they can be used to enhance your data querying capabilities.

BigQuery supports a variety of functions that can be used to manipulate and analyze data. These functions can be used to perform a variety of tasks:

  • Data transformation functions can be used to transform data from one format into another. For example, you could use a function to convert a CSV file into a JSON file.
  • Machine learning functions can be used to train and deploy machine learning models. For example, you could use a function to train a model to predict customer churn.
  • Functions can be used to perform analytics on large datasets. For example, you could...
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