Mastering DevOps: A Cloud Engineering and Data Science Perspective addresses the challenge of understanding and implementing DevOps in an era of rapid technological advancement where cloud-based infrastructure and data science applications have become integral to many organizations. The book covers the specific requirements of these fields, such as scalability, automation, and managing large-scale data and containerized applications. Content focuses on DevOps principles while integrating core technologies such as cloud computing, microservices, and continuous integration/continuous delivery (CI/CD). Additionally, the book provides coverage of a DevOps approach tailored to data science by covering recent advancements and explaining their relevance in a DevOps environment.
Specific topics cover fundamental principles, including history, planning, and essential tools like Git, introduce the core technologies and architectures that power modern DevOps, such as microservices, cloud computing, and containerization, and focus on the practical implementation of DevOps, exploring key practices like continuous integration, automation, and monitoring. Finally, the book delves into advanced topics and future trends, such as deployment strategies and the extension of DevOps principles to data science and other narrowed-down domains.
1. Chapter 1. Introduction to DevOps - p. 3 2. Chapter 2. Planning - p. 27 3. Chapter 3. Version control system - p. 45 4. Unit 2. Core technologies and architectures - p. P2 5. Chapter 4. Microservices - p. 65 6. Chapter 5. Cloud computing for DevOps - p. 91 7. Chapter 6. Packaging and shipping with containers - p. 113 8. Chapter 7. Containers orchestration system - p. 137 9. Unit 3. CI/CD practices and automation - p. P3 10. Chapter 8. Continuous integration, delivery, and deployment - p. 165 11. Chapter 9. Continuous testing - p. 193 12. Chapter 10. Monitoring in DevOps - p. 217 13. Chapter 11. Automation - p. 241 14. Unit 4. Advanced topics and data science perspective - p. P4 15. Chapter 12. Deployment models - p. 263 16. Chapter 13. DataOps - p. 287 17. Index - p. 307
Mastering DevOps: A Cloud Engineering and Data Science Perspective addresses the challenge of understanding and implementing DevOps in an era of rapid technological advancement where cloud-based infrastructure and data science applications have become integral to many organizations. The book covers the specific requirements of these fields, such as scalability, automation, and managing large-scale data and containerized applications. Content focuses on DevOps principles while integrating core technologies such as cloud computing, microservices, and continuous integration/continuous delivery (CI/CD). Additionally, the book provides coverage of a DevOps approach tailored to data science by covering recent advancements and explaining their relevance in a DevOps environment.
Specific topics cover fundamental principles, including history, planning, and essential tools like Git, introduce the core technologies and architectures that power modern DevOps, such as microservices, cloud computing, and containerization, and focus on the practical implementation of DevOps, exploring key practices like continuous integration, automation, and monitoring. Finally, the book delves into advanced topics and future trends, such as deployment strategies and the extension of DevOps principles to data science and other narrowed-down domains.
1. Chapter 1. Introduction to DevOps - p. 3 2. Chapter 2. Planning - p. 27 3. Chapter 3. Version control system - p. 45 4. Unit 2. Core technologies and architectures - p. P2 5. Chapter 4. Microservices - p. 65 6. Chapter 5. Cloud computing for DevOps - p. 91 7. Chapter 6. Packaging and shipping with containers - p. 113 8. Chapter 7. Containers orchestration system - p. 137 9. Unit 3. CI/CD practices and automation - p. P3 10. Chapter 8. Continuous integration, delivery, and deployment - p. 165 11. Chapter 9. Continuous testing - p. 193 12. Chapter 10. Monitoring in DevOps - p. 217 13. Chapter 11. Automation - p. 241 14. Unit 4. Advanced topics and data science perspective - p. P4 15. Chapter 12. Deployment models - p. 263 16. Chapter 13. DataOps - p. 287 17. Index - p. 307
