From DevOps to DevDataOps: Data Management in DevOps processes
Antonio Capizzi, Salvatore Distefano, Manuel Mazzara
TL;DR
This paper explores how BigData and DataOps can enhance data management in DevOps processes, addressing challenges and proposing solutions for handling diverse and large-scale data in software development and operations.
Contribution
It introduces a comprehensive view of data management in DevOps, integrating BigData and DataOps trends to improve efficiency and insights in software development.
Findings
BigData solutions can effectively manage DevOps data artifacts.
DataOps trends are shaping new approaches to DevOps data management.
Challenges include data variety, velocity, and integration complexity.
Abstract
DevOps is a quite effective approach for managing software development and operation, as confirmed by plenty of success stories in real applications and case studies. DevOps is now becoming the main-stream solution adopted by the software industry in development, able to reduce the time to market and costs while improving quality and ensuring evolvability and adaptability of the resulting software architecture. Among the aspects to take into account in a DevOps process, data is assuming strategic importance, since it allows to gain insights from the operation directly into the development, the main objective of a DevOps approach. Data can be therefore considered as the fuel of the DevOps process, requiring proper solutions for its management. Based on the amount of data generated, its variety, velocity, variability, value and other relevant features, DevOps data management can be mainly…
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