Intelligent Cloud-Native Frameworks for Data Reconciliation Master Data Management and Performance Optimization
Abstract
The rapid growth of enterprise data ecosystems has increased the complexity of managing, reconciling, and optimizing data across distributed cloud environments. Traditional data management architectures often struggle with scalability, real-time synchronization, data quality assurance, and performance efficiency. Intelligent cloud-native frameworks have emerged as a transformative solution by leveraging cloud computing, artificial intelligence (AI), machine learning (ML), microservices, containerization, and automation technologies. These frameworks provide scalable and resilient infrastructures capable of supporting large-scale data reconciliation processes, master data management (MDM), and performance optimization initiatives. Data reconciliation ensures consistency and accuracy among heterogeneous data sources, while master data management establishes a unified and trusted view of critical business entities. Cloud-native architectures enhance these processes through automated workflows, real-time analytics, event-driven processing, and distributed data governance mechanisms. Additionally, AI-powered optimization techniques improve system performance by dynamically allocating resources, predicting workloads, and reducing operational costs. The integration of intelligent cloud-native frameworks enables organizations to achieve higher data quality, operational agility, and business intelligence capabilities. This study examines the architectural principles, technological components, methodologies, advantages, and limitations of intelligent cloud-native frameworks in supporting modern enterprise data ecosystems. The findings highlight their significant role in enabling efficient, scalable, and reliable data management strategies in digital transformation initiatives.
Article Information
Journal |
International Journal of Emerging Trends in Engineering and Management Research |
|---|---|
Volume (Issue) |
Vol. 6 No. 3 (2021): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) |
DOI |
|
Pages |
9641-9648 |
Published |
May 6, 2021 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Patrik Fahlström (%2021). Intelligent Cloud-Native Frameworks for Data Reconciliation Master Data Management and Performance Optimization. International Journal of Emerging Trends in Engineering and Management Research , Vol. 6 No. 3 (2021): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 9641-9648. https://doi.org/10.15662/ijetemr.2021.0603001 |
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