Authors : N. Kavya Sri; G. R. Yagna Chaitanya; S. Abhiram; Dr. Shaikshavali Shaik

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/3mcvzeae

Scribd : https://tinyurl.com/5t4nfzv6

DOI : https://doi.org/10.38124/ijisrt/26mar807

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The explosive growth of financial data in modern institutions has created significant challenges in data management, scalability, and analytical processing. Traditional on-premises data processing systems often suffer from high infrastructure costs, complex maintenance, limited scalability, and slower analytical performance, making them inefficient for handling large and continuously growing financial datasets. To address these challenges, this paper presents a serverless data lake architecture for financial analysis using cloud computing technologies. The proposed system leverages cloud services such as Amazon S3, AWS Glue, Amazon Athena, and Amazon QuickSight to build a scalable and efficient analytics platform. Financial datasets collected from open-source sources such as Kaggle are ingested into Amazon S3, forming the storage layer of the data lake. The architecture follows a structured Extract–Transform–Load (ETL) pipeline, where AWS Glue performs automated data extraction, transformation, and conversion of raw datasets into optimized Parquet columnar format, while also creating a metadata catalog for efficient data discovery. Analytical queries are executed using Amazon Athena, which enables serverless SQL-based querying directly on the stored datasets. The resulting insights are visualized through interactive dashboards using Amazon QuickSight, allowing users to explore financial patterns and trends effectively. The proposed architecture eliminates the need for server management while providing high scalability, improved query performance, and cost-efficient data processing. Compared with traditional data processing systems, the serverless approach offers greater flexibility, reduced operational overhead, and faster analytical capabilities. These advantages make the proposed solution a highly effective and practical framework for large-scale financial data analytics in modern cloud environments.

Keywords : Serverless Computing , Scalability, ETL Pipeline , Data Lake Architecture.

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