Book Image

Multi-Cloud Strategy for Cloud Architects - Second Edition

By : Jeroen Mulder
Book Image

Multi-Cloud Strategy for Cloud Architects - Second Edition

By: Jeroen Mulder

Overview of this book

Are you ready to unlock the full potential of your enterprise with the transformative power of multi-cloud adoption? As a cloud architect, you understand the challenges of navigating the vast array of cloud services and moving data and applications to public clouds. But with 'Multi-Cloud Strategy for Cloud Architects, Second Edition', you'll gain the confidence to tackle these complexities head-on. This edition delves into the latest concepts of BaseOps, FinOps, and DevSecOps, including the use of the DevSecOps Maturity Model. You'll learn how to optimize costs and maximize security using the major public clouds - Azure, AWS, and Google Cloud. Examples of solutions by the increasingly popular Oracle Cloud Infrastructure (OCI) and Alibaba Cloud have been added in this edition. Plus, you will discover cutting-edge ideas like AIOps and GreenOps. With practical use cases, including IoT, data mining, Web3, and financial management, this book empowers you with the skills needed to develop, release, and manage products and services in a multi-cloud environment. By the end of this book, you'll have mastered the intricacies of multi-cloud operations, financial management, and security. Don't miss your chance to revolutionize your enterprise with multi-cloud adoption.
Table of Contents (23 chapters)
21
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22
Index

Summary

AIOps was a new kid on the block but, since 2020, it has emerged as an almost essential platform for managing complex cloud environments. AIOps systems help organizations in detecting changes and anomalies in their IT environments and already predicting 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...