Engineering Sustainable Data Architectures for Modern Financial Institutions †

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Detalles Bibliográficos
Publicado en:	Electronics vol. 14, no. 8 (2025), p. 1650
Autor principal:	Ionescu Sergiu-Alexandru
Otros Autores:	Diaconita Vlad, Andreea-Oana, Radu
Publicado:	MDPI AG
Materias:	Technological change Data processing Big Data Data systems Relational data bases Data integration Automation Machine learning Infrastructure Workloads Processing speed Financial institutions Batch processing Banking Innovations Customers Data analysis Modernization Velocity Online transaction processing Ingestion Artificial intelligence Costs Prediction models Pipelining (computers) Business intelligence Optimization Databases Compliance Real time Cloud computing Financial services
Acceso en línea:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Descripción
Resumen:	Modern financial institutions now manage increasingly advanced data-related activities and place a growing emphasis on environmental and energy impacts. In financial modeling, relational databases, big data systems, and the cloud are integrated, taking into consideration resource optimization and sustainable computing. We suggest a four-layer architecture to address financial data processing issues. The layers of our design are for data sources, data integration, processing, and storage. Data ingestion processes market feeds, transaction records, and customer data. Real-time data are captured by Kafka and transformed by Extract-Transform-Load (ETL) pipelines. The processing layer is composed of Apache Spark for real-time data analysis, Hadoop for batch processing, and an Machine Learning (ML) infrastructure that supports predictive modeling. In order to optimize access patterns, the storage layer includes various data layer components. The test results indicate that the processing of market data in real-time, compliance reporting, risk evaluations, and customer analyses can be conducted in fulfillment of environmental sustainability goals. The metrics from the test deployment support the implementation strategies and technical specifications of the architectural components. We also looked at integration models and data flow improvements, with applications in finance. This study aims to enhance enterprise data architecture in the financial context and includes guidance on modernizing data infrastructure.
ISSN:	2079-9292
DOI:	10.3390/electronics14081650
Fuente:	Advanced Technologies & Aerospace Database