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Data Wrangling on AWS

You're reading from  Data Wrangling on AWS

Product type Book
Published in Jul 2023
Publisher Packt
ISBN-13 9781801810906
Pages 420 pages
Edition 1st Edition
Languages
Authors (3):
Navnit Shukla Navnit Shukla
Profile icon Navnit Shukla
Sankar M Sankar M
Profile icon Sankar M
Sampat Palani Sampat Palani
Profile icon Sampat Palani
View More author details

Table of Contents (19) Chapters

Preface Part 1:Unleashing Data Wrangling with AWS
Chapter 1: Getting Started with Data Wrangling Part 2:Data Wrangling with AWS Tools
Chapter 2: Introduction to AWS Glue DataBrew Chapter 3: Introducing AWS SDK for pandas Chapter 4: Introduction to SageMaker Data Wrangler Part 3:AWS Data Management and Analysis
Chapter 5: Working with Amazon S3 Chapter 6: Working with AWS Glue Chapter 7: Working with Athena Chapter 8: Working with QuickSight Part 4:Advanced Data Manipulation and ML Data Optimization
Chapter 9: Building an End-to-End Data-Wrangling Pipeline with AWS SDK for Pandas Chapter 10: Data Processing for Machine Learning with SageMaker Data Wrangler Part 5:Ensuring Data Lake Security and Monitoring
Chapter 11: Data Lake Security and Monitoring Index 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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