METADATA-DRIVEN ETL FRAMEWORKS: A PARADIGM SHIFT IN REAL-TIME ANALYTICS ARCHITECTURE
DOI:
https://doi.org/10.34218/IJCET_16_01_205Keywords:
Metadata-driven Architecture, Real-time Analytics, ETL Automation, Data Pipeline Orchestration, Enterprise Data IntegrationAbstract
This article presents a comprehensive analysis of metadata-driven ETL frameworks in the context of real-time analytics, addressing the growing demand for automated and scalable data processing solutions. The article explores the architectural principles, implementation strategies, and industry applications of metadata-driven approaches in modern data environments. By examining various case studies across financial services, e-commerce, and healthcare sectors, the article demonstrates how these frameworks enhance data pipeline automation, ensure data quality, and optimize processing efficiency. The article highlights the critical role of metadata repositories, transformation rule engines, and dynamic orchestration in achieving scalable real-time analytics capabilities. Our findings indicate that metadata-driven frameworks significantly reduce manual intervention while improving pipeline adaptability and maintenance. The article also discusses current challenges and future directions, providing valuable insights for practitioners and researchers in the field of real-time data analytics. This article contributes to the growing body of knowledge in enterprise data integration and automated ETL processes, offering practical recommendations for organizations seeking to implement metadata-driven solutions.
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Copyright (c) 2025 Sudhakar Reddy Vyza (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
