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

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.