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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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Table of Contents (20) Chapters

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

Sharing data with multiple AWS accounts using AWS Lake Formation permissions

In this section, you will learn how to share data with multiple AWS accounts using AWS Lake Formation permissions.

Lake Formation permission model

As you learned in the previous section, there are challenges in managing S3 bucket policies and Glue Data Catalog resource policies. AWS Lake Formation is the service that is designed to overcome those challenges and simplify data platform management. Lake Formation provides a central layer for defining, classifying, tagging, and managing fine-grained access control to the AWS Glue Data Catalog and Amazon S3 locations. The permission model is designed in an RDBMS-like style so that you can grant permissions on databases, tables, or columns instead of S3 objects. Once you have granted access to tables with Lake Formation permissions, Lake Formation automatically manages both data access and metadata access under the hood, so you don’t need to manually...

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