Omslagsbillede

Engineering Sustainable Data Architectures for Modern Financial Institutions †

Guardado en:
Udgivet i:Electronics vol. 14, no. 8 (2025), p. 1650
Hovedforfatter: Ionescu Sergiu-Alexandru
Andre forfattere: Diaconita Vlad, Andreea-Oana, Radu
Udgivet:
MDPI AG
Fag:
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
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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