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Serverless ETL and Analytics with AWS Glue

You're reading from  Serverless ETL and Analytics with AWS Glue

Product type Book
Published in Aug 2022
Publisher Packt
ISBN-13 9781800564985
Pages 434 pages
Edition 1st Edition
Languages
Authors (6):
Vishal Pathak Vishal Pathak
Profile icon Vishal Pathak
Subramanya Vajiraya Subramanya Vajiraya
Profile icon Subramanya Vajiraya
Noritaka Sekiyama Noritaka Sekiyama
Profile icon Noritaka Sekiyama
Tomohiro Tanaka Tomohiro Tanaka
Profile icon Tomohiro Tanaka
Albert Quiroga Albert Quiroga
Profile icon Albert Quiroga
Ishan Gaur Ishan Gaur
Profile icon Ishan Gaur
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Toc

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 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

Data mesh

While cheap, durable storage helped in storing vast volumes of data, this data had to be secured properly. Since data from a vast variety of sources is stored in the lake, it becomes difficult to define the ownership and management of this data. This requirement resulted in a paradigm of serving data as a product and setting the ownership of the product. This thought process led to the creation of the data mesh.

Data meshes ensure that data lakes don’t become another monolith that the organization’s IT teams now have to manage. This decentralization leads to the democratization of data, which fuels innovation without hindering access to the data. Although data is decentralized and offered as a service, the permission model that’s applied to create a data lake ensures interoperability to reduce the barriers to accessing data products for users that have the right permissions.

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Serverless ETL and Analytics with AWS Glue
Published in: Aug 2022 Publisher: Packt ISBN-13: 9781800564985
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