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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 transformation

Data processing for ML primarily includes data transformation. At its core, SageMaker Data Wrangler includes over 300 built-in transformations that are commonly used for cleaning, transforming, and featurizing your data specifically for data science and ML. Using these built-in transformations, you can transform columns within your dataset without having to write any code. In addition to these built-in transformations, you can add custom transformations using PySpark, Python, pandas, and PySpark SQL. Some of these transformations operate in place, while others create a new output column in your dataset. Whenever you incorporate a transform into your data flow, it introduces a new step in the process. Each added transform modifies your dataset and generates a fresh data frame as a result. Subsequently, any subsequent transforms you apply will be performed on this updated data frame. In the real world, datasets are often imbalanced. This imbalance can be in the form...

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