ETL for Real-Time Financial Analytics: Architectures and Challenges

Registration ID: IJNRD_300832

Published ID: IJNRD1906003

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Abhilash Katari, Manohar Nalmala

Real-time analytics, ETL pipelines, financial analytics, data quality, data consistency, scalability, data latency, security, financial data integration, real-time data processing.

Building ETL (Extract, Transform, Load) pipelines for real-time financial analytics presents unique architectural challenges and considerations. Financial data is inherently complex, encompassing a vast array of formats, sources, and velocity, making the need for robust and efficient ETL processes critical. This article delves into the core architectural elements essential for creating ETL pipelines that can handle the dynamic and fast-paced nature of financial data. Real-time financial analytics demands an architecture that can ingest large volumes of data at high speeds while ensuring data accuracy and integrity. Key architectural considerations include the choice of data storage solutions, integration of scalable and flexible data processing frameworks, and the implementation of low-latency data transformation techniques. Additionally, the architecture must support fault tolerance and data security, given the sensitive nature of financial information. Challenges in building such ETL pipelines are multifaceted. Ensuring data consistency and reliability in real-time scenarios can be difficult, especially when dealing with disparate data sources. The need for continuous monitoring and error handling mechanisms is paramount to maintain the quality of analytics. Furthermore, the integration of advanced technologies such as machine learning and artificial intelligence adds another layer of complexity to the ETL process, requiring specialized skills and resources. This article aims to provide insights into the best practices and common pitfalls associated with designing ETL architectures for real-time financial analytics. By addressing these architectural considerations and challenges, organizations can better harness the power of their financial data, driving more informed and timely business decisions.

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Abhilash Katari & Manohar Nalmala (June-2019). ETL for Real-Time Financial Analytics: Architectures and Challenges. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 4(6), 26-40. https://ijnrd.org/papers/IJNRD1906003.pdf

Volume 4 Issue 6, June-2019

Pages : 26-40

Paper Reg. ID: IJNRD_300832

Published Paper Id: IJNRD1906003

Research Area: Science and Technology

Author Type: Indian Author

Country: -, -, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD1906003.pdf

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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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