The Role of Automation in DevOps: A Study of Tools and Best Practices
Aiyenitaju, Kikelomo (2024)
2024
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024121937652
https://urn.fi/URN:NBN:fi:amk-2024121937652
Tiivistelmä
This thesis explores the role of automation in optimizing DevOps practices, focusing on tools and best practices that enhance efficiency, speed, and reliability in software delivery. A qualitative research approach was employed, using a systematic literature review from academic journals, industry reports, and case studies. The research examines key automation tools such as Jenkins, Docker, and Kubernetes, emphasizing their impact on Continuous Integration (CI), Continuous Delivery (CD), and Infrastructure as Code (IaC) processes.
The findings indicate that automation significantly improves DevOps workflows by reducing manual tasks, speeding up deployment cycles, and fostering better collaboration between development and operations teams. However, challenges such as tool integration, security risks, and resistance to change remain prevalent. The study highlights the socio-technical implications of automation, underscoring the need to address technological and cultural factors for successful implementation.
Practical recommendations are provided to help organizations overcome these challenges, including adopting standardized tools, enhanced training programs, and fostering a culture of continuous improvement. The study also suggests areas for future research, such as exploring the integration of Artificial Intelligence (AI) and Machine Learning (ML) in automation, assessing the scalability of automation tools in large environments, and understanding the socio-technical aspects influencing automation in DevOps. These insights aim to assist DevOps practitioners, researchers, and organizations refine their automation strategies for improved efficiency and resilience.
The findings indicate that automation significantly improves DevOps workflows by reducing manual tasks, speeding up deployment cycles, and fostering better collaboration between development and operations teams. However, challenges such as tool integration, security risks, and resistance to change remain prevalent. The study highlights the socio-technical implications of automation, underscoring the need to address technological and cultural factors for successful implementation.
Practical recommendations are provided to help organizations overcome these challenges, including adopting standardized tools, enhanced training programs, and fostering a culture of continuous improvement. The study also suggests areas for future research, such as exploring the integration of Artificial Intelligence (AI) and Machine Learning (ML) in automation, assessing the scalability of automation tools in large environments, and understanding the socio-technical aspects influencing automation in DevOps. These insights aim to assist DevOps practitioners, researchers, and organizations refine their automation strategies for improved efficiency and resilience.