FinTech Cloud-based data lakes: Performance, governance, and scalability

Abstract

The rapid adoption of cloud-based data lakes and warehouses in financial institutions has transformed data management, enabling the handling of vast datasets critical for decision-making in trading, risk management, and customer analytics. This paper examines the pivotal roles of performance, governance, and scalability in the successful deployment of these systems as of March 2025. Performance is analyzed through the lens of query optimization and real-time analytics, highlighting how technologies like distributed computing enhance efficiency. Governance is explored with a focus on regulatory compliance, data security, and the implementation of robust frameworks to safeguard sensitive financial data against breaches and ensure adherence to global standards such as GDPR and Basel III. Scalability is evaluated as a core benefit, addressing the ability of cloud systems to dynamically adapt to fluctuating data demands while maintaining cost-effectiveness. The study synthesizes current industry practices, technological advancements, and challenges, revealing the interdependence of these three dimensions. Findings suggest that while cloud-based solutions offer significant advantages, financial institutions must navigate challenges such as data migration, latency in hybrid models, and governance complexities in multi-cloud environments. This paper contributes to the discourse on cloud adoption in finance by providing actionable insights for optimizing performance, ensuring compliance, and achieving scalable data architectures in an increasingly digital financial landscape.