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Data Wrangling on AWS

You're reading from   Data Wrangling on AWS Clean and organize complex data for analysis

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Product type Paperback
Published in Jul 2023
Publisher Packt
ISBN-13 9781801810906
Length 420 pages
Edition 1st Edition
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Authors (3):
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Sankar M Sankar M
Author Profile Icon Sankar M
Sankar M
Navnit Shukla Navnit Shukla
Author Profile Icon Navnit Shukla
Navnit Shukla
Sam Palani Sam Palani
Author Profile Icon Sam Palani
Sam Palani
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Table of Contents (19) Chapters Close

Preface 1. Part 1:Unleashing Data Wrangling with AWS
2. Chapter 1: Getting Started with Data Wrangling FREE CHAPTER 3. Part 2:Data Wrangling with AWS Tools
4. Chapter 2: Introduction to AWS Glue DataBrew 5. Chapter 3: Introducing AWS SDK for pandas 6. Chapter 4: Introduction to SageMaker Data Wrangler 7. Part 3:AWS Data Management and Analysis
8. Chapter 5: Working with Amazon S3 9. Chapter 6: Working with AWS Glue 10. Chapter 7: Working with Athena 11. Chapter 8: Working with QuickSight 12. Part 4:Advanced Data Manipulation and ML Data Optimization
13. Chapter 9: Building an End-to-End Data-Wrangling Pipeline with AWS SDK for Pandas 14. Chapter 10: Data Processing for Machine Learning with SageMaker Data Wrangler 15. Part 5:Ensuring Data Lake Security and Monitoring
16. Chapter 11: Data Lake Security and Monitoring 17. Index 18. Other Books You May Enjoy

5 Vs of big data

The 5 Vs of big data are five key characteristics that define the concept of big data. These characteristics help to understand the nature of big data and how it can be effectively analyzed and used. Let’s look at these in more detail, as follows:

  • Volume: Big data refers to extremely large datasets that are too large to be processed using traditional methods. These datasets can range from a few terabytes to several petabytes in size.

For example, Twitter alone generates over 500 million tweets per day, which amounts to a large volume of data that must be stored, processed, and analyzed. Another example of big data would be data generated by large e-commerce companies such as Amazon. This data may include customer purchase history, website clickstream data, and customer service interactions. This data can be collected from various sources such as online transactions, mobile apps, social media, emails, and customer service interactions. All of this...

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