Optimizing ETL Pipelines for High-Performance Financial Reporting
Optimizing ETL Pipelines for High-Performance Financial Reporting
International Journal of Computer Techniques – Volume 8 Issue 6, November – December 2021
ISSN: 2394-2231 | https://ijctjournal.org
Abstract
This paper outlines a comprehensive strategy for optimizing Extract, Transform, Load (ETL) pipelines within the context of financial institutions. It emphasizes scalable data transformations, incremental loading, architecture design, observability, and regulatory compliance. The framework aims to support **fast, accurate, and secure financial reporting** by adopting industry best practices and leveraging cloud-native and open-source technologies.
Keywords
ETL Optimization, Financial Reporting Pipelines, Incremental Data Extraction, Scalable Data Transformation, Data Governance, Regulatory Compliance, Spark ETL
Conclusion
Optimized ETL pipelines are vital to delivering **compliant, timely, and accurate financial insights**. By embracing pipeline observability, resilient architecture, and toolchains like **Apache Spark** and **Google Cloud Dataflow**, organizations can streamline compliance efforts, minimize latency, and unlock operational intelligence. Cross-functional trust is further achieved through integrated governance and monitoring mechanisms.
References
- Moses, B. (2021). Building Reliable Data Pipelines. Monte Carlo.
- Apache Spark Documentation (2021). spark.apache.org
- FINRA (2021). Reporting Guidelines. FINRA website
- Oracle (2021). SOX Compliance with Oracle Cloud. Oracle Whitepaper
- Google Cloud (2021). Dataflow Best Practices. cloud.google.com
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