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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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Toc

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 discovery with QuickSight

Amazon QuickSight supports loading data from various data sources, and we can then create visuals in the Analyses tab to understand the data. Data discovery can also be done using Jupyter notebooks with custom visualization libraries, but that might require programming expertise and complex setup before performing data discovery activities. In contrast, business users can perform data discovery in QuickSight with visuals in the Analyses tab.

QuickSight-supported data sources and setup

QuickSight supports a wide variety of data sources. The complete list can be found at https://docs.aws.amazon.com/quicksight/latest/user/supported-data-sources.html.

The sources could be classified into the following broad categories:

  • Relational data sources: Covering cloud and on-premises data sources, including popular data engines such as MySQL, Postgres, SQL Server, Oracle, Snowflake, and Redshift. When connecting to on-premises data sources, you need...
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