Book Image

Multi-Cloud Architecture and Governance

By : Jeroen Mulder
Book Image

Multi-Cloud Architecture and Governance

By: Jeroen Mulder

Overview of this book

Multi-cloud has emerged as one of the top cloud computing trends, with businesses wanting to reduce their reliance on only one vendor. But when organizations shift to multiple cloud services without a clear strategy, they may face certain difficulties, in terms of how to stay in control, how to keep all the different components secure, and how to execute the cross-cloud development of applications. This book combines best practices from different cloud adoption frameworks to help you find solutions to these problems. With step-by-step explanations of essential concepts and practical examples, you’ll begin by planning the foundation, creating the architecture, designing the governance model, and implementing tools, processes, and technologies to manage multi-cloud environments. You’ll then discover how to design workload environments using different cloud propositions, understand how to optimize the use of these cloud technologies, and automate and monitor the environments. As you advance, you’ll delve into multi-cloud governance, defining clear demarcation models and management processes. Finally, you’ll learn about managing identities in multi-cloud: who’s doing what, why, when, and where. By the end of this book, you’ll be able to create, implement, and manage multi-cloud architectures with confidence
Table of Contents (28 chapters)
1
Section 1 – Introduction to Architecture and Governance for Multi-Cloud Environments
7
Section 2 – Getting the Basics Right with BaseOps
12
Section 3 – Cost Control in Multi-Cloud with FinOps
17
Section 4 – Security Control in Multi-Cloud with SecOps
22
Section 5 – Structured Development on Multi-Cloud Environments with DevOps

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...