Citation:

Gomathi Shirdi Botla, 2023. "Implementing Seamless Financial Data Injection Into Data Lakes Using Kafka", ESP Journal of Engineering & Technology Advancements, 3(4): 115-116.

Abstract:

In the modern financial sector, data plays a pivotal role in decision-making, compliance, and operational efficiency. However, managing financial data streams effectively remains a significant challenge due to the diversity of data sources, volume, and the need for real-time processing. Traditional methods for updating and consuming data in financial systems are fraught with latency, inconsistency, and scalability issues. This paper explores the application of Apache Kafka for seamless financial data injection into data lakes. By leveraging Kafka’s distributed architecture, the proposed approach addresses bottlenecks in financial data ingestion and integration, enabling real-time processing, scalability, and enhanced system reliability. The discussion includes a detailed problem analysis, a unique implementation strategy, practical applications, and an assessment of its impact and scope within the financial industry. This paper contributes to academic and industry discussions by proposing a novel method of utilizing Kafka’s stream processing capabilities to harmonize disparate financial data streams into a unified data lake.

References:

[1] J. Kreps, N. Narkhede, and J. Rao, “Kafka: A Distributed Messaging System for Log Processing,” Proceedings of the 6th International Workshop on Networking Meets Databases, Athens, Greece, 2011.

[2] M. Kleppmann, Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, 1st ed. Sebastopol, CA: O'Reilly Media, 2017.

[3] J. Dean and S. Ghemawat, “MapReduce: Simplified Data Processing on Large Clusters,” Communications of the ACM, vol. 51, no. 1, pp. 107–113, Jan. 2008.

[4] P. Goyal and S. Goel, “Stream Processing in Apache Kafka: A Hands-on Guide,” International Journal of Computer Applications, vol. 179, no. 8, pp. 5–11, Dec. 2017.

[5] Neuman and K. Krishnamurthy, “Real-Time Data Integration in Financial Systems,” Journal of Financial Data Science, vol. 3, no. 2, pp. 12–22, 2020.

[6] Reed, “Optimizing Data Lakes for Financial Analytics,” Data Engineering Journal, vol. 10, no. 4, pp. 22–30, 2019.

[7] R. Gupta, “Distributed Systems and Event Streaming: A Case for Apache Kafka,” IEEE Transactions on Big Data, vol. 6, no. 3, pp. 215–227, Sept. 2022.

Keywords:

Financial Data, Data Lakes, Apache Kafka, Real-Time Processing, Scalability, Financial Systems Integration, Data Streaming.