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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

Data visualization with QuickSight

In this section, we will explore different visuals and options that are available within QuickSight. We will explore high-level concepts and different visuals and features within QuickSight in this section.

Visualization and charts with QuickSight

The following steps are performed when publishing a dashboard in QuickSight.

Figure 8.24: QuickSight – high-level dashboard publishing workflow

Figure 8.24: QuickSight – high-level dashboard publishing workflow

  1. Create data source: This is the step where the connection to the data source is established. This can be a one-time activity and can be reused multiple times when new datasets are created from the data source.
  2. Create dataset: A dataset can be created from a new data source or existing datasets for different analysis requirements. The datasets can be modified, parsed, or enriched based on specific analysis requirements.
  3. Create analysis (create visuals): Create visuals from a specific dataset. Here, the dashboard...
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