Modern Enterprise Data Platforms: Architectural Patterns and Operational Strategies for Scalable Data Processing
DOI:
https://doi.org/10.32628/CSEIT2511651Keywords:
enterprise data management, Extract, Transform, and Load (ETL), Hadoop Distributed File System, ETL Processing Framework, Data ValidationAbstract
The rapid expansion of enterprise data ecosystems has driven organizations to adopt robust and scalable data platform architectures capable of supporting diverse analytical and regulatory requirements. This paper examines the design and operational characteristics of modern enterprise data platforms deployed within large financial organizations, emphasizing the coexistence of traditional data warehouses and distributed big data environments. By analyzing production-scale implementations, the study highlights architectural patterns, processing frameworks, and governance mechanisms that support reliable data ingestion, transformation, and analytics. The findings illustrate how enterprises can manage heterogeneous data sources at scale while preserving data accuracy, auditability, and regulatory compliance through structured ETL frameworks, automated data validation pipelines, and hybrid deployment models spanning on-premises and cloud infrastructures.
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