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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

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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