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Modern Data Architecture on AWS

You're reading from  Modern Data Architecture on AWS

Product type Book
Published in Aug 2023
Publisher Packt
ISBN-13 9781801813396
Pages 420 pages
Edition 1st Edition
Languages
Author (1):
Behram Irani Behram Irani
Profile icon Behram Irani
Toc

Table of Contents (24) Chapters close

Preface 1. Part 1: Foundational Data Lake
2. Prologue: The Data and Analytics Journey So Far 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

Summary

This chapter was all about ensuring that the data platform that’s built is performant as well as cost-effective. We started by understanding the need for a data platform that operates optimally. If any part of the data platform is either not performing well or is very expensive, it often creates a snowball effect and affects the business negatively.

A lot of cost optimization can be achieved by optimizing the infrastructure used by the AWS services under the covers. By optimizing storage and compute resources, we can save significant costs. We also looked at some of the tools AWS provides that help in the cost optimization process.

Finally, we looked at some of the service-specific tuning settings that can help with performance improvements. The list of such improvements can be quite long for each service, but the key message was to leverage the best practices for each service and always perform a WAR before deploying workloads in production.

In the next and...

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