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Multi-Cloud Architecture and Governance

You're reading from   Multi-Cloud Architecture and Governance Leverage Azure, AWS, GCP, and VMware vSphere to build effective multi-cloud solutions

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Product type Paperback
Published in Dec 2020
Publisher Packt
ISBN-13 9781800203198
Length 412 pages
Edition 1st Edition
Tools
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Authors (2):
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Jeroen Mulder Jeroen Mulder
Author Profile Icon Jeroen Mulder
Jeroen Mulder
Jeroen Mulder Jeroen Mulder
Author Profile Icon Jeroen Mulder
Jeroen Mulder
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Toc

Table of Contents (28) Chapters Close

Preface 1. Section 1 – Introduction to Architecture and Governance for Multi-Cloud Environments
2. Chapter 1: Introduction to Multi-Cloud FREE CHAPTER 3. Chapter 2: Business Acceleration Using a Multi-Cloud Strategy 4. Chapter 3: Getting Connected – Designing Connectivity 5. Chapter 4: Service Designs for Multi-Cloud 6. Chapter 5: Managing the Enterprise Cloud Architecture 7. Section 2 – Getting the Basics Right with BaseOps
8. Chapter 6: Designing, Implementing, and Managing the Landing Zone 9. Chapter 7: Designing Resilience and Performance 10. Chapter 8: Defining Automation Tools and Processes 11. Chapter 9: Defining and Using Monitoring and Management Tools 12. Section 3 – Cost Control in Multi-Cloud with FinOps
13. Chapter 10: Managing Licenses 14. Chapter 11: Defining Principles for Resource Provisioning and Consumption 15. Chapter 12: Defining Naming Conventions and Tagging 16. Chapter 13: Validating and Managing Bills 17. Section 4 – Security Control in Multi-Cloud with SecOps
18. Chapter 14: Defining Security Policies 19. Chapter 15: Implementing Identity and Access Management 20. Chapter 16: Defining Security Policies for Data 21. Chapter 17: Implementing and Integrating Security Monitoring 22. Section 5 – Structured Development on Multi-Cloud Environments with DevOps
23. Chapter 18: Designing and Implementing CI/CD Pipelines 24. Chapter 19: Introducing AIOps in Multi-Cloud 25. Chapter 20: Introducing Site Reliability Engineering in Multi-Cloud 26. Assessments 27. Other Books You May Enjoy

Securing raw data for big data modeling 

One of the big advantages of the public cloud is the huge capacity that these platforms offer. Together with the increasing popularity of public clouds, the industry saw another major development in the possibilities to gather and analyze vast amounts of data, without the need to build an infrastructure themselves in on-premises data centers to host the data. With public clouds, companies can have enormous data lakes at their disposal. Data analysts program their analytical models to these data lakes. This is what is referred to as big data. Big data modeling is about four Vs:

  • Volume: The quantity of data
  • Variety: The different types of data
  • Veracity: The quality of data
  • Velocity: The speed of processing data

Data analysts often add a fifth V to these four, and that's value. Big data gets value when data is analyzed and processed in such a way that it actually means something. The four-V model is shown in...

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