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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

Overview of data sharing strategies

At the time of writing, depending on the organizations and use cases, there are different ways to share data. There are three typical strategies for sharing data:

  • Single tenant
  • Hub and spoke
  • Data mesh

In this section, you will learn about each of these strategies and discuss their backgrounds, challenges, and benefits.

Single tenant

Data lakes have become a popular approach for people who want to store and query data in a centralized repository. It allows you to store all the structured data, semi-structured data, and unstructured data at any scale. Here, cloud storage such as Amazon S3 fits well with data lakes because there are no data size limits. You do not need to convert your data into a predefined fixed schema in advance. Instead, you can just ingest data as-is. When you want to analyze the data, you can easily convert the data into your preferred schema on the fly, then analyze it on top of the data lake.

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