Mariana, Oliveira and Rakesh, Iyer and Thomas, Walker (2023) MIGRATING BFSI DATA WORKLOADS TO CLOUD-NATIVE ENVIRONMENTS A CASE STUDY ON MULTI-TIER DATA LAKEHOUSE ARCHITECTURES WITH AWS REDSHIFT, ATHENA, AND INTELLIGENT ORCHESTRATION FOR COMPLIANCE. Journal of Engineering, Mechanics and Modern Architecture, 2 (11). pp. 50-61. ISSN 2181-4384

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Abstract

The rapid digitization of the Banking, Financial Services, and Insurance (BFSI) sector has intensified the demand for secure, scalable, and compliant data infrastructure. Traditional on-premises data warehouses in BFSI environments often struggle with siloed architectures, high operational costs, and limited agility in meeting evolving regulatory requirements such as GDPR, PCI DSS, and RBI/SEC reporting mandates. This article presents a case study on migrating BFSI data workloads to a cloud-native, multi-tier data lakehouse architecture leveraging AWS Redshift, Amazon Athena, and intelligent orchestration frameworks. The study highlights the architectural shift from legacy ETL pipelines to serverless, query-on-demand ecosystems that unify structured and unstructured data across regulatory, risk management, and customer analytics workloads. Using a combination of Redshift for high-performance OLAP, Athena for schema-on-read flexibility, and AWS Glue/Airflow for automated orchestration, the proposed design demonstrates how BFSI enterprises can achieve near real-time data availability while maintaining audit-ready compliance. Intelligent orchestration with event-driven pipelines reduced batch-to-query latency by up to 65%, while automated data lineage tracking improved regulator-facing transparency. Operational benchmarks from the case study show a 40% reduction in infrastructure costs compared to on-premises data warehouses, alongside a 50% improvement in query performance for risk and fraud analytics workloads. Moreover, embedded compliance controls such as encryption-at-rest (KMS), fine-grained access policies (IAM/Lake Formation), and GDPR-ready audit trails ensured adherence to multi-jurisdictional data governance mandates.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	18 Sep 2025 07:06
Last Modified:	18 Sep 2025 07:06
URI:	http://eprints.umsida.ac.id/id/eprint/16352

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