Citation:

Prasenjit Banerjee, Rajarshi Roy, Chalamayya Batchu, Piyush Ranjan, 2023. "Examining the Application of Data Federation across Cloud Databases in the Financial Services Domain" ESP Journal of Engineering & Technology Advancements  3(3): 148-153.

Abstract:

Data federation is emerging as a critical strategy for integrating and querying data across diverse cloud data sources, offering a unified view without necessitating data migration. This paper explores the efficiency and considerations involved in implementing data federation across cloud environments. We analyze the performance impacts, including query response times and resource utilization, and discuss strategies to optimize federated queries. Additionally, we address security and compliance concerns, emphasizing data governance and access controls. In the financial services domain, data federation can significantly enhance real-time risk management and fraud detection by providing seamless access to disparate data sources without the need for data duplication. Through case studies and experimental evaluations, we demonstrate how data federation can enhance data accessibility and agility, while identifying best practices for ensuring efficient and secure data integration. This study provides valuable insights for organizations seeking to leverage multi-cloud architectures, highlighting the balance between performance, cost, and complexity in federated data systems.

References:

[1] Nokkala, Tiina, and Tomi Dahlberg. "Data Federation in the Era of Digital, Consumer-Centric Cares and Empowered Citizens." In Well-Being in the Information Society. Fighting Inequalities: 7th International Conference, WIS 2018, Turku, Finland, August 27-29, 2018, Proceedings 7, pp. 134-147. Springer International Publishing, 2018.

[2] Backeberg, Björn, Zdeněk Šustr, Enol Fernández, Gennadii Donchyts, Arjen Haag, JB Raymond Oonk, Gerben Venekamp, Benjamin Schumacher, Stefan Reimond, and Charis Chatzikyriakou. "An open compute and data federation as an alternative to monolithic infrastructures for big Earth data analytics." Big Earth Data 7, no. 3 (2023): 812-830.

[3] Ethan, Amelia. "Data Virtualization: The Key to Realizing Big Data Analytics Potential." INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 6, no. 2 (2022): 20-50.

[4] Bogdanov, Alexander, Alexander Degtyarev, Nadezhda Shchegoleva, Valery Khvatov, and Vladimir Korkhov. "Evolving principles of big data virtualization." In Computational Science and Its Applications–ICCSA 2020: 20th International Conference, Cagliari, Italy, July 1–4, 2020, Proceedings, Part VI 20, pp. 67-81. Springer International Publishing, 2020.

[5] Agrawal, Gagan. "Data Virtualization." In Encyclopedia of Big Data, pp. 347-348. Cham: Springer International Publishing, 2022.

[6] Chilukoori, Sadha Shiva Reddy, Shashikanth Gangarapu, and Chaitanya Kumar Kadiyala. "OPTIMIZING QUERY PERFORMANCE IN CLOUD DATA WAREHOUSES: A FRAMEWORK FOR IDENTIFYING AND ADDRESSING PERFORMANCE BOTTLENECKS."

[7] Endris, Kemele M., Philipp D. Rohde, Maria-Esther Vidal, and Sören Auer. "Ontario: Federated query processing against a semantic data lake." In Database and Expert Systems Applications: 30th International Conference, DEXA 2019, Linz, Austria, August 26–29, 2019, Proceedings, Part I 30, pp. 379-395. Springer International Publishing, 2019

[8] Giebler, Corinna, Christoph Gröger, Eva Hoos, Holger Schwarz, and Bernhard Mitschang. "Leveraging the data lake: current state and challenges." In Big Data Analytics and Knowledge Discovery: 21st International Conference, DaWaK 2019, Linz, Austria, August 26–29, 2019, Proceedings 21, pp. 179-188. Springer International Publishing, 2019.

[9] Akhtar, Usman, Anita Sant’Anna, Chang-Ho Jihn, Muhammad Asif Razzaq, Jaehun Bang, and Sungyoung Lee. "A cache-based method to improve query performance of linked Open Data cloud." Computing 102 (2020): 1743-1763.

[10] Vijay, Vandana, and Ruchi Nanda. "Query caching technique over cloud-based MapReduce system: A survey." In Rising Threats in Expert Applications and Solutions: Proceedings of FICR-TEAS 2020, pp. 19-25. Springer Singapore, 2021.

[11] Husain, Mohammad, James McGlothlin, Mohammad M. Masud, Latifur Khan, and Bhavani M. Thuraisingham. "Heuristics-based query processing for large RDF graphs using cloud computing." IEEE Transactions on Knowledge and Data Engineering 23, no. 9 (2011): 1312-1327.

[12] Obiniyi, A. A., Rosemary M. Dzer, and S. E. Abdullahi. "Balancing Query Performance and Security on Relational Cloud Database: Architecture." International Journal of Computer Applications 118, no. 15 (2015).

[13] Ge, Xing, Bin Yao, Minyi Guo, Changliang Xu, Jingyu Zhou, Chentao Wu, and Guangtao Xue. "LSShare: an efficient multiple query optimization system in the cloud." Distributed and Parallel Databases 32 (2014): 583-605.

[14] Somasundaram, Prakash. "Cloud Storage Strategies for High-Performance Analytics: An In-Depth Look at Databases, Data Warehouses, and Object Storage Solutions."

Keywords:

Data Federation, BYOL, Query Performance, Query Optimization.