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Serverless ETL and Analytics with AWS Glue

You're reading from   Serverless ETL and Analytics with AWS Glue Your comprehensive reference guide to learning about AWS Glue and its features

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Product type Paperback
Published in Aug 2022
Publisher Packt
ISBN-13 9781800564985
Length 434 pages
Edition 1st Edition
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Authors (6):
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Vishal Pathak Vishal Pathak
Author Profile Icon Vishal Pathak
Vishal Pathak
Ishan Gaur Ishan Gaur
Author Profile Icon Ishan Gaur
Ishan Gaur
Tomohiro Tanaka Tomohiro Tanaka
Author Profile Icon Tomohiro Tanaka
Tomohiro Tanaka
Albert Quiroga Albert Quiroga
Author Profile Icon Albert Quiroga
Albert Quiroga
Subramanya Vajiraya Subramanya Vajiraya
Author Profile Icon Subramanya Vajiraya
Subramanya Vajiraya
Noritaka Sekiyama Noritaka Sekiyama
Author Profile Icon Noritaka Sekiyama
Noritaka Sekiyama
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Table of Contents (20) Chapters Close

Preface 1. Section 1 – Introduction, Concepts, and the Basics of AWS Glue
2. Chapter 1: Data Management – Introduction and Concepts FREE CHAPTER 3. Chapter 2: Introduction to Important AWS Glue Features 4. Chapter 3: Data Ingestion 5. Section 2 – Data Preparation, Management, and Security
6. Chapter 4: Data Preparation 7. Chapter 5: Data Layouts 8. Chapter 6: Data Management 9. Chapter 7: Metadata Management 10. Chapter 8: Data Security 11. Chapter 9: Data Sharing 12. Chapter 10: Data Pipeline Management 13. Section 3 – Tuning, Monitoring, Data Lake Common Scenarios, and Interesting Edge Cases
14. Chapter 11: Monitoring 15. Chapter 12: Tuning, Debugging, and Troubleshooting 16. Chapter 13: Data Analysis 17. Chapter 14: Machine Learning Integration 18. Chapter 15: Architecting Data Lakes for Real-World Scenarios and Edge Cases 19. Other Books You May Enjoy

Chapter 14: Machine Learning Integration

Machine learning (ML) is one of the cornerstones of today’s computing for any software-related company. ML models are capable of making predictions or deductions based on past experience, provided as training data. This enables a wide variety of applications with large benefits to any organization.

Because it relies on training data, ML is closely tied to data mining, data processing, and, in general, any kind of extract, transform, load (ETL) process. Training data must be properly cleaned, formatted, and classified before it can be fed to a model – a process that greatly affects the effectiveness of the model itself. Because of this, services such as AWS Glue offer ML-specific features and integrations, catered to making ML easier and more effective to use.

Training data preparation is not the only relationship ML has with ETL processes – it can also be used to enhance and provide new transformations within the processes...

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