An J. Cloud Computing and Big Data Technology 2ed 2025 torrent

Textbook in PDF format

This book comprehensively introduces the basic knowledge and main technologies of Cloud Computing and Big Data. It covers Cloud Computing, Big Data technology, virtualization technology, data centers, parallel computing and clustering technology, cloud storage technology, OpenStack, Hadoop, Spark, Storm, and cloud computing simulation. With a focus on practicality, this volume is enriched with experiments and closely integrates theory with practice, enabling readers to systematically and comprehensively understand Cloud Computing and Big Data technologies. This useful reference textbook benefits professionals, academics, researchers, graduate and undergraduate students in databases/information sciences.
Compared to the first edition, this revision features significant additions, deletions, and structural adjustments. For instance, content on data center deployment and site selection has been streamlined to enhance conciseness. A new Chapter 7 has been added, focusing on Docker container technology and the container orchestration platform Kubernetes, both widely adopted in enterprise IT today. Chapters 8–10 reorganize the introduction to Big Data frameworks, starting with batch processing to guide readers into the world of Big Data processing. Technical frameworks have also been updated — for example, Hadoop 3.0 with its architectural changes has been introduced, and Flink has been added to the Storm chapter for comparative learning — to present readers with more advanced technologies.
The book consists of 11 chapters
Chapter 1: Overview of Cloud Computing introduces the definition, development background, infrastructure, service models, deployment modes, typical cloud computing products, recent advancements, and the current state of China’s cloud computing industry.
Chapter 2: Overview of Big Data Technology covers the emergence of big data, the “4V” characteristics of big data, major applications, driving forces in the industry, key technologies, and typical big data computing architectures.
Chapter 3: Virtualization Technology provides a detailed introduction to virtualization, including its principles, common solutions, and practical applications.
Chapter 4: Data Centers and Cloud Storage discusses the basic concepts of data centers, trends in the era of cloud computing and big data, and focuses on cloud storage—its definition, architecture, foundational technologies, and features.
Chapter 5: Parallel Computing and Cluster Technology explores parallel computing fundamentals, clusters as the backbone of cloud infrastructure, classifications of parallel computing, related technologies, and hands-on MPI programming.
Chapter 6: OpenStack introduces the architecture and key modules of OpenStack, a powerful IaaS platform.
Chapter 7: Docker and Kubernetes covers Docker commands, image management, and container operations and provides an introduction to Kubernetes for container orchestration.
Chapter 8: Hadoop details the Hadoop ecosystem, including HDFS, MapReduce, HBase, and environment setup for distributed big data development.
Chapter 9: Storm and Flink explains Storm’s principles, architecture, Storm–Yarn integration, environment configuration, and real-time stream processing applications, with a comparative introduction to Flink.
Chapter 10: Spark focuses on the Spark in-memory computing framework, covering its mechanisms, execution modes, RDDs, processing paradigms, and ecosystem.
Preface
Overview of Cloud Computing
Overview of Big Data Technologies
Virtualization
Data Centers and Cloud Storage Technology
Parallel Computing and Cluster Technology
OpenStack: The Powerful Platform of IaaS
Docker: A Versatile Container Technology
Hadoop: A Distributed Big Data Development Platform
Storm: A Topology-Based Real-Time Stream Processing Framework
Spark: An In-Memory Big Data Computing Framework
Cloud Computing Simulation