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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 quality validation

Data quality validation is an important phase in data pipelines as it ensures the correctness of the data used in analyses. Without correct data, even if you use good analytical tools, the analytical insights will be incorrect. So, customers/developers need to focus more on the data quality phase to create accurate datasets for further analysis.

What is the difference between data quality and data cleansing? Some of us might be confused between data cleansing and data quality validation. In reality, there will be some overlap between the two phases, and some activities are used interchangeably:

  • Data cleansing is the phase where we clean and deduplicate data and identify generic data issues, such as splitting data for more meaningful analysis, cleansing data errors, and so on. Without cleansing, the data might not be useful for analysis efforts. For example, in a student database and results table, the score column can have non-numeric values or missing...
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