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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
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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

Data import

Before you start to process your data using SageMaker Data Wrangler, you first need to import data into Data Wrangler. Using Data Wrangler, you can connect and import data from a variety of data stores. When you start Data Wrangler for the first time, the first screen you get asks whether you want to import data or use a sample dataset:

Figure 4.1 – Data Wrangler import

Figure 4.1 – Data Wrangler import

Amazon S3 is an object-based data store that has quickly become the de facto storage of the internet. Due to its low cost per GB and high levels of reliability, you can store and retrieve any amount of data, at any time, from anywhere on the web using Amazon S3. You can upload and access data both using the console or programmatically using APIs, which is also the most common way to work with data in Amazon S3. Amazon S3 implements bucket and object architecture. You can think of a bucket as a folder and objects as files that are logically stored inside the bucket. SageMaker...

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