Published Paper Details:

The rapid growth of financial data has created a strong demand for scalable and efficient systems for data storage, processing, and analysis. Traditional data processing approaches often rely on complex infrastructure, leading to high operational costs and limited scalability. To address these challenges, this paper proposes a serverless data lake architecture for real-time financial analytics using cloud-based services. The proposed system leverages Amazon S3 for scalable data storage, AWS Glue for automated schema discovery and data cataloging, Amazon Athena for serverless querying, and Amazon QuickSight for interactive data visualization. By eliminating the need for infrastructure management, the architecture simplifies deployment while ensuring high scalability, flexibility, and cost efficiency. A financial dataset is utilized to evaluate the system's performance and analytical capabilities, including trend analysis, volume analysis, and price comparison. The results demonstrate that serverless data lake architectures provide an effective and practical solution for modern financial analytics, enabling faster insights with reduced operational overhead. The architecture also supports dynamic scaling based on workload demands, making it suitable for varying data volumes.