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

Managing the foundation for data lakes

Data engineers design, build and manage the data pipelines, but the foundation of the data lake and data warehouse is the specific landing zone for the data platform. Typically, landing zones in cloud are operated by cloud engineers who take care of the compute, storage, and network resources.

Looking at management of data platforms, we can distinguish various roles:

  • Data architect or engineer: the architect and data engineer are often combined in one role. The role is responsible for design, development, and deployment of the data pipelines. The engineer must have extensive knowledge of ETL or ELT principles and technologies, making sure that data from sources get collected and transformed into usable datasets in data warehouses or other data products where the data can be further analyzed. Data also needs to be validated, which is a required skill of the engineer too. In essence, the engineer makes sure that data that is ingested into warehouses...