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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

Step 2 – importing data

Before we can start importing data into SageMaker Data Wrangler, we need to create a connection with our data source. SageMaker Data Wrangler provides out-of-the-box native connectors to Amazon S3, Amazon Athena, Amazon Redshift, Snowflake, Amazon EMR, and Databricks. Besides that, you can also set up new data sources with over 40 SaaS and web applications using Amazon AppFlow, a fully managed integration service that helps you securely transfer data between software as a service (SaaS) applications. The Create connection screen shows the connectors in Data Wrangler, along with additional data sources you can set up using Amazon AppFlow.

Figure 10.5: Data Wrangler data sources

Figure 10.5: Data Wrangler data sources

In this chapter, we will use a publicly available example, the Titanic dataset. The Titanic dataset is considered the “Hello World” of machine learning datasets due to the number of commonly used data processing and machine learning techniques...

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