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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 7: Metadata Management

Just as with relational databases, AWS Glue relies on the concepts of databases and tables to organize and manage datasets. That said, these concepts are quite different in their execution. In a relational database, the data to be stored and its descriptors (such as the schema and comments, also known as metadata) are stored and managed together: there is no way to store data without describing it first, and there is no way to add metadata to already written data.

In big data environments, the storage and metadata layers are decoupled. There is no centralized storage system because of dataset size limitations, and data is typically dumped without a format onto distributed, large-scale systems such as Apache Hadoop or Amazon S3. This means we as users have to bring the metadata to the data wherever it is stored, cataloging the data and specifying its location, how to read it, and how to understand it (its schema).

In the case of Glue, this centralized...

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