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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

Concepts

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

Technical requirements

In this chapter, we will use the NYPD (Police Department) Motor Vehicle Collisions – Crashes dataset (https://data.cityofnewyork.us/Public-Safety/Motor-Vehicle-Collisions-Crashes/h9gi-nx95) provided by the New York Open Data Catalog: https://opendata.cityofnewyork.us.

You may wish to load this data into BigQuery to work through the cleansing and transformation examples provided in this chapter as being hands-on with this chapter’s content will help reinforce the concepts. You may reference the hands-on exercise loading data guide in Chapter 4, Loading and Transforming Data. The raw data is available at the preceding link, as well as this book’s GitHub repository: https://github.com/PacktPublishing/Data-Exploration-and-Preparation-with-BigQuery/releases. You can download the dataset locally, upload it to a Google Cloud Storage (GCS) bucket, then create a new table in BigQuery using the GCS bucket and file as a source.

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Authors (1)

Mike Kahn

Mike Kahn is a data and infrastructure enthusiast and currently leads a Customer Engineering team at Google Cloud. Prior to Google, Mike spent five years in solution architecture roles and worked in operations and leadership roles in the data center industry. His over 15 years of experience have given him a deep knowledge of data and infrastructure engineering, operations, strategy, and leadership. Mike holds multiple Google Cloud certifications and is a lifelong learner. He is based in Boca Raton, Florida, in the US and holds a Bachelor of Science degree in Management Information Systems (MIS) from University of Central Florida and a Master of Science degree in MIS from Florida International University.

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