Cloud Data Engineering Strategies for Large-Scale Financial Data Integration and Intelligent Corporate Performance Reporting
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V3I4P119Keywords:
Cloud Data Engineering, Financial Data Integration, Corporate Performance Reporting, Data Lakehouse, Data Warehouse, Etl/Elt, Real-Time Analytics, Business Intelligence, Machine Learning, Financial Analytics, Cloud ComputingAbstract
Despite the presence of powerful ERP, CRM, banking, regulatory, market, etc. applications, enterprise financial data is increasing rapidly making it difficult for organizations to get timely, accurate and importantly, actionable corporate financial insights from the data. The coming age of high volumes, velocities, and varieties of financial information can overwhelm traditional data integration and reporting architectures, causing data silos, reports with delays, governance issues, and poor analytical power. This paper introduces a comprehensive cloud data engineering framework to enable efficient integration of structured and unstructured financial data at scale into a cloud-native architecture coupled with automated ETL/ELT pipelines, data lakehouse architecture, and a real-time analytics platform that can deliver intelligent, corporate performance reports. The strategy proposed combines heterogeneous financial data to create a unified data environment, ensuring data quality, security, compliance, and scalability. Moreover, an intelligent reporting solution is presented based on sophisticated techniques in advanced analytics, machine learning and business intelligence technologies that automates performance monitoring, financial forecasts, anomaly detection and executive decision making. Experimental evaluation and enterprise deployment analysis show that the resulting architectures offer significant gains on data processing, reporting latency, scalability, and decision making effectiveness over the existing reporting architectures. The study proposes a complete model for an architectural solution that can integrate concepts of cloud data engineering and financial aspects with the use of intelligent analysis. The results show that companies that make the switch to cloud-native financial data platforms can benefit from more transparency in operations, quicker reporting cycles, better regulatory compliance, and better strategic planning. The framework will be a key reference for companies looking to embark on digital transformation projects, and as AI evolves, it will form the basis for more sophisticated financial intelligence systems and automated corporate performance management tools.
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