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

Summary

AIOps is the new kid on the block. These are complex systems that help organizations in detecting changes and anomalies in their IT environments and already predict what impact these events might have on other components within their environments. AIOps systems can even predict this from planned changes coming from DevOps systems such as CI/CD pipelines. To be able to do that, AIOps makes use of big data analysis: it has access to a lot of different data sources, inside and outside IT environments. This data is analyzed and fed into algorithms: this is where AI comes in, and ML. AIOps systems learn so that they can actually predict future events.

AIOps are complex systems that require vast investments from vendors and thus from companies that want to start working with AIOps. However, most organizations want to become more and more data-driven, meaning that data is driving all decisions. This makes a company more agile and faster in responding to market changes.

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