CLOUD-NATIVE DATA ENGINEERING MATURITY ASSESSMENT FOR ENTERPRISE ANALYTICS PLATFORMS

Abstract

Enterprise organizations increasingly adopt cloud-native data engineering platforms to support large-scale analytics, artificial intelligence, and data-driven decision-making. However, evaluating the maturity level of data engineering capabilities remains challenging due to complex architectures, rapidly evolving technologies, distributed data environments, and changing business requirements. This paper proposes a Cloud-Native Data Engineering Maturity Assessment Framework for Enterprise Analytics Platforms by integrating cloud computing, data engineering practices, MLOps principles, data governance, automation, scalability assessment, and analytics capability evaluation. The proposed framework assesses key maturity dimensions including data architecture, pipeline automation, data quality management, security, governance, cloud scalability, operational intelligence, and analytics readiness. Machine learning-based assessment mechanisms analyze enterprise data engineering characteristics and provide maturity insights for continuous improvement. Experimental evaluation demonstrates improvements in platform assessment accuracy, operational visibility, scalability evaluation, governance effectiveness, and strategic decision-making. The proposed framework provides a structured approach for organizations to evaluate and enhance cloud-native data engineering capabilities. Keywords— Cloud-Native Data Engineering, Maturity Assessment, Enterprise Analytics, Data Platforms, Cloud Computing, Data Governance, MLOps, Analytics Transformation.