The Complete Cloud Computing Handbook

Take your cloud expertise to the next level by mastering the technologies that power modern data platforms, real-time analytics, and enterprise intelligence across AWS, Azure, and Google Cloud.



Data has become the foundation of every successful digital organization. From customer interactions and financial transactions to artificial intelligence and machine learning systems, modern enterprises rely on cloud-native data platforms to collect, process, analyze, and secure massive volumes of information. Organizations need professionals who can design scalable data architectures, build resilient data pipelines, manage cloud databases, implement streaming solutions, and transform raw information into actionable business intelligence.



The Complete Cloud Computing Handbook - Part II provides a comprehensive deep dive into cloud databases, data engineering, analytics, data lakes, data warehouses, lakehouse architectures, real-time streaming systems, and business intelligence platforms. Designed for cloud engineers, data engineers, cloud architects, analytics professionals, developers, and certification candidates, this volume bridges the gap between cloud infrastructure and data-driven decision-making.



The book then guides you through the design and implementation of enterprise-grade data ingestion and integration pipelines. You will discover how modern organizations collect, transform, validate, and integrate data from diverse sources while ensuring reliability, scalability, and governance throughout the data lifecycle.



As your knowledge expands, you will explore the architecture and implementation of data lakes, cloud data warehouses, and modern lakehouse platforms. You will learn how organizations store and process structured, semi-structured, and unstructured data while enabling analytics, machine learning, and business intelligence workloads at scale.



What You Will Learn

Design and deploy cloud-native database solutions across AWS, Azure, and Google Cloud.

Understand relational, NoSQL, document, graph, and wide-column database architectures.

Build scalable data ingestion and integration pipelines.

Implement ETL and ELT workflows for enterprise data processing.

Design modern data lakes, data warehouses, and lakehouse architectures.

Develop cloud-native data engineering workflows.

Build real-time streaming and event-processing systems.

Implement analytics solutions using cloud-native services.

Create business intelligence dashboards and reporting platforms.

Optimize data storage, performance, scalability, and cost management.

Apply security, governance, and compliance best practices to cloud data environments.

Understand enterprise data architecture patterns and implementation strategies.

Compare AWS, Azure, and Google Cloud data services and capabilities.

Gain practical experience through real-world cloud data engineering projects.

Build valuable skills aligned with modern cloud and data engineering careers.

Who This Book Is For



This book is ideal for:



Cloud engineers seeking advanced data platform expertise.

Data engineers building cloud-native analytics solutions.

Cloud architects designing enterprise-scale data ecosystems.

Database administrators transitioning to cloud environments.

Business intelligence and analytics professionals.

Software developers working with cloud data services.

DevOps and platform engineers supporting data workloads.

Cloud certification candidates pursuing advanced technical skills.

Technology professionals interested in modern data engineering and analytics architectures.