DataOps: Revolutionizing Application Development through Data-Centric Methodologies

Lakshmi Narayana Gupta Koralla

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025, vol. 11, issue 2, 2155-2167

Abstract: This comprehensive article analysis examines Data-Oriented Application Development (DataOps). This transformative methodology integrates data management with software engineering principles to create more efficient, reliable, and value-driven data ecosystems. The article explores the theoretical foundations of DataOps, tracing its evolution from traditional data management approaches and examining its relationship to adjacent methodologies like DevOps and Agile. It provides a detailed examination of core architectural components—including pipeline automation, CI/CD integration, governance frameworks, collaboration models, and observability systems—that collectively enable organizations to treat data as a first-class asset throughout the application development lifecycle. The research offers implementation guidance through organizational prerequisites, technological requirements, and phased adoption strategies supported by case studies of successful transformations across industries. Particular attention is given to measuring DataOps success through multidimensional metrics frameworks and benchmarking approaches that connect technical improvements to business outcomes. The economic implications are analyzed through cost-benefit models that capture immediate operational efficiencies and long-term strategic value. Finally, the article examines emerging trends at the intersection of DataOps with advanced technologies, ethical considerations in automated data processing, and future research directions. It provides a holistic view of how DataOps is reshaping how organizations leverage their data assets for competitive advantage.

Keywords: DataOps; Data Pipeline Automation; CI/CD for Data; Data Governance; Real-time Data Processing (search for similar items in EconPapers)
Date: 2025
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT23112576
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v11:y2025:i2:id:1271

DOI: 10.32628/CSEIT23112576

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