Agile Methodologies in Data Engineering Projects: Promoting Collaboration and Adaptability

Jagadeesh Thiruveedul¹ & Prof. (Dr) Sangeet Vashishtha²

¹Jawaharlal Nehru Technological University

Kakinada,Andhra Pradesh 533003 India

²Dept. of Computer Application

IIMT University

Meerut, India

Abstract— Agile methods, which were initially designed for software development, have increasingly been applied to data engineering initiatives. Agile methods focus on iterative development, flexibility, and teamwork, which are especially beneficial in the context of data-driven projects. Data engineering, which typically entails large-scale systems, intricate data pipelines, and data integration from multiple sources, can gain from the flexibility and ongoing feedback that are part of Agile methodologies. This paper discusses the application of Agile methods in data engineering, with an emphasis on how they facilitate teamwork among cross-functional teams and flexibility in response to changing data requirements. Though it has its benefits, studies show that there are still issues with embracing Agile in data engineering completely. The conventional Agile methods like Scrum or Kanban tend to be suited for small teams of software developers, hence challenging to implement in large and complicated data infrastructure projects. Besides, incorporating Agile with data governance, real-time data processing, and AI-based model development is still an untapped area. There is a notable research gap in knowing how Agile frameworks can be adapted to various scales of data engineering projects, particularly those handling big data, cloud systems, or machine learning pipelines. This paper integrates results from multiple studies carried out between 2015 and 2024, which show the development of Agile practices in data engineering, their influence on project results, and the necessary adaptations to scale these approaches successfully. Through filling these gaps, it seeks to give a better understanding of Agile’s potential in promoting collaboration and flexibility in data engineering teams as well as proposing methods to overcome current limitations.

Keywords– Agile methods, data engineering, teamwork, flexibility, Scrum, Kanban, data pipelines, big data, cloud computing, machine learning, real-time processing, data governance, iterative development, scalability, cross-functional teams.

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