| Summary |
Recent advances in Big Data Analytics (BDA) have stimulated widespread interest to integrate BDA capabilities into all aspects of a business. Before these advances, companies have spent time optimizing the software development process and best practices associated with application development. These processes include project management structures and how to deliver new features of an application to its customers efficiently. While these processes are significant for application development, they cannot be utilized effectively for the software development of Big Data Analytics. Instead, some practices and technologies enable automation and monitoring across the full lifecycle of productivity from design to deployment and operations of Analytics. This paper builds on those practices and technologies and introduces a highly scalable framework for Big Data Analytics development operations. This framework builds on top of the best-known processes associated with DevOps. These best practices are then shown using a NoSQL cloud-based platform that consumes and processes structured and unstructured real-time data. As a result, the framework produces scalable, timely, and accurate analytics in real-time, which can be easily adjusted or enhanced to meet the needs of a business and its customers. |
| General note | Presented to the Faculty of the Department of Computer Science |
| General note | Advisor: Venkat Gudivada |
| General note | Title from PDF t.p. (June 24, 2021). |
| Dissertation note | M.S. East Carolina University 2020. |
| Bibliography note | Includes bibliographical references. |
| Technical details | System requirements: Adobe Reader. |
| Technical details | Mode of access: World Wide Web. |
