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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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Toc

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

Data lake layers

Now that we have a broader business use case for setting up a data lake, let’s look at a use case that will help us define what the different layers of a typical data lake are and why they are required.

Use case for creating data lake layers

GreatFin has different LOBs, and within each of these LOBs, multiple personas have different tasks to perform on the data. Each persona may need specific access to different sets of data. They will all need the data to be formatted and stored in a certain way for them to do their day-to-day operations easily. For example, data engineers may need access to the raw source data so that they can profile the data and understand the quality of the data. Data scientists may need access to a standardized form of datasets so that they can do feature engineering for creating machine learning (ML) models. Data analysts may need access to business-friendly datasets so that they can derive insights from the data.

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