Sarcouncil Journal of Engineering and Computer Sciences

Abstract: The digital age of digital transformation has brought about unprecedented challenges to organizations dealing with enormous volumes of data using conventional ETL frameworks. Legacy architectures illustrate inherent architectural constraints when handling contemporary data workloads, suffering severe performance degradation and operational inefficiencies. Conventional monolithic ETL architectures are challenged by elastic scaling demands, have resource consumption rates under optimal levels, and have a huge manual maintenance overhead. Cloud-native data engineering is an innovative answer, taking advantage of microservices architecture, serverless computation models, and distributed processing engines to overcome the limitations of legacy systems. Contemporary cloud environments ensure end-to-end service ecosystems for autonomic scaling, cost-optimized storage offerings, and event-based processing functionalities. Microservices breakdown allows component development and deployment independence, and serverless architecture removes infrastructure maintenance hassles. Distributed computing platforms enable bulk data processing by cluster paradigms that support diverse programming languages and purpose-built algorithms. Migration designs highlight incremental transformation methods for minimizing operational risks using phased implementation strategies. Evaluation frameworks analyze dependencies on existing infrastructure, volumes of data, and performance attributes to optimize migration sequences. Technology integration includes object storage services, Function-as-a-Service platforms, and MapReduce processing engines. Operational excellence requires Infrastructure as Code principles, end-to-end data quality monitoring, and advanced schema evolution management. The refactoring supports companies to realize better scalability, cost optimization, and processing capabilities while ensuring data integrity and system stability over distributed landscapes.