Book Image

Architecting Cloud Computing Solutions

By : Kevin L. Jackson, Scott Goessling
Book Image

Architecting Cloud Computing Solutions

By: Kevin L. Jackson, Scott Goessling

Overview of this book

Cloud adoption is a core component of digital transformation. Scaling the IT environment, making it resilient, and reducing costs are what organizations want. Architecting Cloud Computing Solutions presents and explains critical cloud solution design considerations and technology decisions required to be made for deploying the right cloud service and deployment models, based on your business and technology service requirements. This book starts with the fundamentals of cloud computing and its architectural concepts. It then walks you through cloud service models (IaaS, PaaS, and SaaS), deployment models (public, private, community, and hybrid) and implementation options (enterprise, MSP, and CSP) to explain and describe the key considerations and challenges organizations face during cloud migration. Later, this book delves into how to leverage DevOps, Cloud-Native, and serverless architectures in your cloud environment and presents industry best practices for scaling your cloud environment. Finally, this book addresses in depth how to manage essential cloud technology service components, such as data storage, security controls, and disaster recovery. By the end of this book, you will have mastered all the design considerations and operational trades required to adopt cloud services, no matter which cloud service provider you choose.
Table of Contents (24 chapters)
Free Chapter
Hands-On Lab 1 – Basic Cloud Design (Single Server)
Hands-On Lab 3 – Optimizing Current State (12 Months Later)
Cloud Architecture – Lessons Learned


Database PaaS services typically align as either a SQL/relational form or NoSQL/non-relational type.

SQL/relational databases handled data comprising large numbers of similar data elements. These elements have identifiable dependencies among each other. When this structured data is queried, users make certain assumptions about the data structure and the relationship consistency between the retrieved data elements.

Data elements are recorded in tables, where each column represents a data element attribute. Table columns may also embed dependencies for how entries in one table column relate to a corresponding column in a different table. These dependencies are strictly enforced during any data manipulation.

In No-SQL/non-relational databases (Mongo, Map Reduce, and so on), an enforced database structure does not exist. This is useful when processing large data sets and...