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Modern Data Architecture on AWS

You're reading from   Modern Data Architecture on AWS A Practical Guide for Building Next-Gen Data Platforms on AWS

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Product type Paperback
Published in Aug 2023
Publisher Packt
ISBN-13 9781801813396
Length 420 pages
Edition 1st Edition
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Author (1):
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Behram Irani Behram Irani
Author Profile Icon Behram Irani
Behram Irani
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Table of Contents (24) Chapters Close

Preface 1. Part 1: Foundational Data Lake
2. Prologue: The Data and Analytics Journey So Far FREE CHAPTER 3. Chapter 1: Modern Data Architecture on AWS 4. Chapter 2: Scalable Data Lakes 5. Part 2: Purpose-Built Services And Unified Data Access
6. Chapter 3: Batch Data Ingestion 7. Chapter 4: Streaming Data Ingestion 8. Chapter 5: Data Processing 9. Chapter 6: Interactive Analytics 10. Chapter 7: Data Warehousing 11. Chapter 8: Data Sharing 12. Chapter 9: Data Federation 13. Chapter 10: Predictive Analytics 14. Chapter 11: Generative AI 15. Chapter 12: Operational Analytics 16. Chapter 13: Business Intelligence 17. Part 3: Govern, Scale, Optimize And Operationalize
18. Chapter 14: Data Governance 19. Chapter 15: Data Mesh 20. Chapter 16: Performant and Cost-Effective Data Platform 21. Chapter 17: Automate, Operationalize, and Monetize 22. Index 23. Other Books You May Enjoy

Internal data sharing

Organizations have many internal LOBs and each LOB has many personas that interact with the data produced by their department. Different LOBs often want access to portions of data from other departments for many reasons, including cross-sell, up-sell, fraud detection, and other critical insights about their customers. First, let’s look at a use case on how each LOB can share data that they have curated inside their S3 data lake.

Data sharing using Amazon Athena

Previously, we covered how you can create a data lake on Amazon S3 and then interactively query it using Amazon Athena. In a simple scenario, the data produced by one LOB is only consumed by the personas inside the same LOB. But to unlock the true value of data, organizations prefer that each LOB shares relevant sets of data with other LOBs. When organizations prefer to create a centralized enterprise data lake, the question becomes, how can each LOB access the datasets that belong to them...

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