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Multi-Cloud Strategy for Cloud Architects - Second Edition

You're reading from  Multi-Cloud Strategy for Cloud Architects - Second Edition

Product type Book
Published in Apr 2023
Publisher Packt
ISBN-13 9781804616734
Pages 470 pages
Edition 2nd Edition
Languages
Author (1):
Jeroen Mulder Jeroen Mulder
Profile icon Jeroen Mulder

Table of Contents (23) Chapters

Preface 1. Introduction to Multi-Cloud 2. Collecting Business Requirements 3. Starting the Multi-Cloud Journey 4. Service Designs for Multi-Cloud 5. Managing the Enterprise Cloud Architecture 6. Controlling the Foundation Using Well-Architected Frameworks 7. Designing Applications for Multi-Cloud 8. Creating a Foundation for Data Platforms 9. Creating a Foundation for IoT 10. Managing Costs with FinOps 11. Maturing FinOps 12. Cost Modeling in the Cloud 13. Implementing DevSecOps 14. Defining Security Policies 15. Implementing Identity and Access Management 16. Defining Security Policies for Data 17. Implementing and Integrating Security Monitoring 18. Developing for Multi-Cloud with DevOps and DevSecOps 19. Introducing AIOps and GreenOps in Multi-Cloud 20. Conclusion: The Future of Multi-Cloud 21. Other Books You May Enjoy
22. Index

Building and sizing a data platform

As with every service that we deploy in cloud, we need something to build a platform on a foundation. Hence, building a landing zone that can hold raw data is the first step. This landing zone should be an environment that serves only one purpose: to capture raw data. It’s recommended to build this landing zone separate from core IT systems. It should be scalable, but at low-cost, since it will hold a lot of data. The issue with keeping data is that it might increase the cloud bill exponentially. Data storage comes at a very low price per unit of data, but the catch is that we need a lot of these small units.

Important is to implement governance from the start. This includes defining and implementing guardrails for classification of data and tagging.

Once the landing zone has been established, data analysts can start using the data lake as a sandbox environment. This is the second stage. Analysts can start building prototypes of data models and...

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